Paternal kin recognition in the high frequency / ultrasonic range in a solitary foraging mammal

Directory of Open Access Journals (Sweden)

Kessler Sharon E

2012-11-01

Full Text Available Abstract Background Kin selection is a driving force in the evolution of mammalian social complexity. Recognition of paternal kin using vocalizations occurs in taxa with cohesive, complex social groups. This is the first investigation of paternal kin recognition via vocalizations in a small-brained, solitary foraging mammal, the grey mouse lemur (Microcebus murinus, a frequent model for ancestral primates. We analyzed the high frequency/ultrasonic male advertisement (courtship call and alarm call. Results Multi-parametric analyses of the calls’ acoustic parameters and discriminant function analyses showed that advertisement calls, but not alarm calls, contain patrilineal signatures. Playback experiments controlling for familiarity showed that females paid more attention to advertisement calls from unrelated males than from their fathers. Reactions to alarm calls from unrelated males and fathers did not differ. Conclusions 1 Findings provide the first evidence of paternal kin recognition via vocalizations in a small-brained, solitarily foraging mammal. 2 High predation, small body size, and dispersed social systems may select for acoustic paternal kin recognition in the high frequency/ultrasonic ranges, thus limiting risks of inbreeding and eavesdropping by predators or conspecific competitors. 3 Paternal kin recognition via vocalizations in mammals is not dependent upon a large brain and high social complexity, but may already have been an integral part of the dispersed social networks from which more complex, kin-based sociality emerged.

8 CFR 292.2 - Organizations qualified for recognition; requests for recognition; withdrawal of recognition...

Science.gov (United States)

2010-01-01

...; requests for recognition; withdrawal of recognition; accreditation of representatives; roster. 292.2...; withdrawal of recognition; accreditation of representatives; roster. (a) Qualifications of organizations. A non-profit religious, charitable, social service, or similar organization established in the United...

Optical Pattern Recognition

Science.gov (United States)

Yu, Francis T. S.; Jutamulia, Suganda

2008-10-01

Contributors; Preface; 1. Pattern recognition with optics Francis T. S. Yu and Don A. Gregory; 2. Hybrid neural networks for nonlinear pattern recognition Taiwei Lu; 3. Wavelets, optics, and pattern recognition Yao Li and Yunglong Sheng; 4. Applications of the fractional Fourier transform to optical pattern recognition David Mendlovic, Zeev Zalesky and Haldum M. Oxaktas; 5. Optical implementation of mathematical morphology Tien-Hsin Chao; 6. Nonlinear optical correlators with improved discrimination capability for object location and recognition Leonid P. Yaroslavsky; 7. Distortion-invariant quadratic filters Gregory Gheen; 8. Composite filter synthesis as applied to pattern recognition Shizhou Yin and Guowen Lu; 9. Iterative procedures in electro-optical pattern recognition Joseph Shamir; 10. Optoelectronic hybrid system for three-dimensional object pattern recognition Guoguang Mu, Mingzhe Lu and Ying Sun; 11. Applications of photrefractive devices in optical pattern recognition Ziangyang Yang; 12. Optical pattern recognition with microlasers Eung-Gi Paek; 13. Optical properties and applications of bacteriorhodopsin Q. Wang Song and Yu-He Zhang; 14. Liquid-crystal spatial light modulators Aris Tanone and Suganda Jutamulia; 15. Representations of fully complex functions on real-time spatial light modulators Robert W. Cohn and Laurence G. Hassbrook; Index.

A new selective developmental deficit: Impaired object recognition with normal face recognition.

Science.gov (United States)

Germine, Laura; Cashdollar, Nathan; Düzel, Emrah; Duchaine, Bradley

2011-05-01

Studies of developmental deficits in face recognition, or developmental prosopagnosia, have shown that individuals who have not suffered brain damage can show face recognition impairments coupled with normal object recognition (Duchaine and Nakayama, 2005; Duchaine et al., 2006; Nunn et al., 2001). However, no developmental cases with the opposite dissociation - normal face recognition with impaired object recognition - have been reported. The existence of a case of non-face developmental visual agnosia would indicate that the development of normal face recognition mechanisms does not rely on the development of normal object recognition mechanisms. To see whether a developmental variant of non-face visual object agnosia exists, we conducted a series of web-based object and face recognition tests to screen for individuals showing object recognition memory impairments but not face recognition impairments. Through this screening process, we identified AW, an otherwise normal 19-year-old female, who was then tested in the lab on face and object recognition tests. AW's performance was impaired in within-class visual recognition memory across six different visual categories (guns, horses, scenes, tools, doors, and cars). In contrast, she scored normally on seven tests of face recognition, tests of memory for two other object categories (houses and glasses), and tests of recall memory for visual shapes. Testing confirmed that her impairment was not related to a general deficit in lower-level perception, object perception, basic-level recognition, or memory. AW's results provide the first neuropsychological evidence that recognition memory for non-face visual object categories can be selectively impaired in individuals without brain damage or other memory impairment. These results indicate that the development of recognition memory for faces does not depend on intact object recognition memory and provide further evidence for category-specific dissociations in visual

Speech Recognition

Directory of Open Access Journals (Sweden)

Adrian Morariu

2009-01-01

Full Text Available This paper presents a method of speech recognition by pattern recognition techniques. Learning consists in determining the unique characteristics of a word (cepstral coefficients by eliminating those characteristics that are different from one word to another. For learning and recognition, the system will build a dictionary of words by determining the characteristics of each word to be used in the recognition. Determining the characteristics of an audio signal consists in the following steps: noise removal, sampling it, applying Hamming window, switching to frequency domain through Fourier transform, calculating the magnitude spectrum, filtering data, determining cepstral coefficients.

Recognition and Toleration

DEFF Research Database (Denmark)

Lægaard, Sune

2010-01-01

Recognition and toleration are ways of relating to the diversity characteristic of multicultural societies. The article concerns the possible meanings of toleration and recognition, and the conflict that is often claimed to exist between these two approaches to diversity. Different forms...... or interpretations of recognition and toleration are considered, confusing and problematic uses of the terms are noted, and the compatibility of toleration and recognition is discussed. The article argues that there is a range of legitimate and importantly different conceptions of both toleration and recognition...

Recognition of cigarette brand names and logos by primary schoolchildren in Ankara, Turkey.

Science.gov (United States)

Emri, S; Bağci, T; Karakoca, Y; Bariş, E

1998-01-01

To assess the smoking behaviour of primary schoolchildren and their ability to recognise brand names and logos of widely advertised cigarettes, compared with other commercial products intended for children. Cross-sectional survey in classroom settings using a questionnaire designed to measure attitudes towards smoking and the recognition of brand names and logos for 16 food, beverage, cigarette, and toothpaste products. Ankara, Turkey. 1093 children (54.6% boys, 44.4% girls) aged 7-13 years (mean = 10, SD = 1), from grades 2-5. The student sample was taken from three primary schools--one school in each of three residential districts representing high, middle, and low income populations. Prevalence of ever-smoking, recognition of brand names and logos. Prevalence of ever-smoking was 11.7% overall (13.9% among boys and 9.1% among girls; p Brand recognition rates ranged from 58.1% for Chee-tos (a food product) to 95.2% for Samsun (a Turkish cigarette brand). Recognition rates for cigarette brand names and logos were 95.2% and 80.8%, respectively, for Samsun; 84.0% and 90.5%, respectively, for Camel; and 92.1% and 69.5%, respectively, for Marlboro. The Camel logo and the Samsun and Marlboro brand names were the most highly recognised of all product logos and brand names tested. The high recognition of cigarette brand names and logos is most likely the result of tobacco advertising and promotion. Our results indicate the need to implement comprehensive tobacco control measures in Turkey.

Humor in print health advertisements: enhanced attention, privileged recognition, and persuasiveness of preventive messages.

Science.gov (United States)

Blanc, Nathalie; Brigaud, Emmanuelle

2014-01-01

This study tested the effect of humor in one particular type of print advertisement: the preventive health ads for three topics (alcohol, tobacco, obesity). Previous research using commercial ads demonstrated that individuals' attention is spontaneously attracted by humor, leading to a memory advantage for humorous information over nonhumorous information. Two experiments investigated whether the positive effect of humor can occur with preventive health ads. In Experiment 1, participants observed humorous and nonhumorous health ads while their viewing times were recorded. In Experiment 2, to compare humorous and nonhumorous ads, the memory of health messages was assessed through a recognition task and a convincing score was collected. The results confirmed that, compared to nonhumorous health ads, those using humor received prolonged attention, were judged more convincing, and their messages were better recognized. Overall, these findings suggest that humor can be of use in preventive health communication.

Re-thinking employee recognition: understanding employee experiences of recognition

OpenAIRE

Smith, Charlotte

2013-01-01

Despite widespread acceptance of the importance of employee recognition for both individuals and organisations and evidence of its increasing use in organisations, employee recognition has received relatively little focused attention from academic researchers. Particularly lacking is research exploring the lived experience of employee recognition and the interpretations and meanings which individuals give to these experiences. Drawing on qualitative interviews conducted as part of my PhD rese...

Use of the recognition heuristic depends on the domain's recognition validity, not on the recognition validity of selected sets of objects.

Science.gov (United States)

Pohl, Rüdiger F; Michalkiewicz, Martha; Erdfelder, Edgar; Hilbig, Benjamin E

2017-07-01

According to the recognition-heuristic theory, decision makers solve paired comparisons in which one object is recognized and the other not by recognition alone, inferring that recognized objects have higher criterion values than unrecognized ones. However, success-and thus usefulness-of this heuristic depends on the validity of recognition as a cue, and adaptive decision making, in turn, requires that decision makers are sensitive to it. To this end, decision makers could base their evaluation of the recognition validity either on the selected set of objects (the set's recognition validity), or on the underlying domain from which the objects were drawn (the domain's recognition validity). In two experiments, we manipulated the recognition validity both in the selected set of objects and between domains from which the sets were drawn. The results clearly show that use of the recognition heuristic depends on the domain's recognition validity, not on the set's recognition validity. In other words, participants treat all sets as roughly representative of the underlying domain and adjust their decision strategy adaptively (only) with respect to the more general environment rather than the specific items they are faced with.

Graphical symbol recognition

OpenAIRE

K.C. , Santosh; Wendling , Laurent

2015-01-01

International audience; The chapter focuses on one of the key issues in document image processing i.e., graphical symbol recognition. Graphical symbol recognition is a sub-field of a larger research domain: pattern recognition. The chapter covers several approaches (i.e., statistical, structural and syntactic) and specially designed symbol recognition techniques inspired by real-world industrial problems. It, in general, contains research problems, state-of-the-art methods that convey basic s...

Statistical Pattern Recognition

CERN Document Server

Webb, Andrew R

2011-01-01

Statistical pattern recognition relates to the use of statistical techniques for analysing data measurements in order to extract information and make justified decisions.  It is a very active area of study and research, which has seen many advances in recent years. Applications such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition, all require robust and efficient pattern recognition techniques. This third edition provides an introduction to statistical pattern theory and techniques, with material drawn from a wide range of fields,

Rotation-invariant neural pattern recognition system with application to coin recognition.

Science.gov (United States)

Fukumi, M; Omatu, S; Takeda, F; Kosaka, T

1992-01-01

In pattern recognition, it is often necessary to deal with problems to classify a transformed pattern. A neural pattern recognition system which is insensitive to rotation of input pattern by various degrees is proposed. The system consists of a fixed invariance network with many slabs and a trainable multilayered network. The system was used in a rotation-invariant coin recognition problem to distinguish between a 500 yen coin and a 500 won coin. The results show that the approach works well for variable rotation pattern recognition.

Consumers recall and recognition for brand symbols

OpenAIRE

Subhani, Muhammad Imtiaz; Hasan, Syed Akif; Osman, Ms. Amber

2012-01-01

Brand Symbols are important for any brand in helping consumers to remember one’s brand at the point of purchase. In advertising different ways are used to grab attention in consumers’ mind and majorly it’s through brand recall and recognition. This research captivates the Brand Symbol concept and determines whether symbols play an important role in creating a differential impact with other brands. Secondly, it also answers that whether brand symbol is the cause of creating positive associatio...

Familiar Person Recognition: Is Autonoetic Consciousness More Likely to Accompany Face Recognition Than Voice Recognition?

Science.gov (United States)

Barsics, Catherine; Brédart, Serge

2010-11-01

Autonoetic consciousness is a fundamental property of human memory, enabling us to experience mental time travel, to recollect past events with a feeling of self-involvement, and to project ourselves in the future. Autonoetic consciousness is a characteristic of episodic memory. By contrast, awareness of the past associated with a mere feeling of familiarity or knowing relies on noetic consciousness, depending on semantic memory integrity. Present research was aimed at evaluating whether conscious recollection of episodic memories is more likely to occur following the recognition of a familiar face than following the recognition of a familiar voice. Recall of semantic information (biographical information) was also assessed. Previous studies that investigated the recall of biographical information following person recognition used faces and voices of famous people as stimuli. In this study, the participants were presented with personally familiar people's voices and faces, thus avoiding the presence of identity cues in the spoken extracts and allowing a stricter control of frequency exposure with both types of stimuli (voices and faces). In the present study, the rate of retrieved episodic memories, associated with autonoetic awareness, was significantly higher from familiar faces than familiar voices even though the level of overall recognition was similar for both these stimuli domains. The same pattern was observed regarding semantic information retrieval. These results and their implications for current Interactive Activation and Competition person recognition models are discussed.

[Prosopagnosia and facial expression recognition].

Science.gov (United States)

Koyama, Shinichi

2014-04-01

This paper reviews clinical neuropsychological studies that have indicated that the recognition of a person's identity and the recognition of facial expressions are processed by different cortical and subcortical areas of the brain. The fusiform gyrus, especially the right fusiform gyrus, plays an important role in the recognition of identity. The superior temporal sulcus, amygdala, and medial frontal cortex play important roles in facial-expression recognition. Both facial recognition and facial-expression recognition are highly intellectual processes that involve several regions of the brain.

Pupil dilation during recognition memory: Isolating unexpected recognition from judgment uncertainty.

Science.gov (United States)

Mill, Ravi D; O'Connor, Akira R; Dobbins, Ian G

2016-09-01

Optimally discriminating familiar from novel stimuli demands a decision-making process informed by prior expectations. Here we demonstrate that pupillary dilation (PD) responses during recognition memory decisions are modulated by expectations, and more specifically, that pupil dilation increases for unexpected compared to expected recognition. Furthermore, multi-level modeling demonstrated that the time course of the dilation during each individual trial contains separable early and late dilation components, with the early amplitude capturing unexpected recognition, and the later trailing slope reflecting general judgment uncertainty or effort. This is the first demonstration that the early dilation response during recognition is dependent upon observer expectations and that separate recognition expectation and judgment uncertainty components are present in the dilation time course of every trial. The findings provide novel insights into adaptive memory-linked orienting mechanisms as well as the general cognitive underpinnings of the pupillary index of autonomic nervous system activity. Copyright © 2016 Elsevier B.V. All rights reserved.

Kernel learning algorithms for face recognition

CERN Document Server

Li, Jun-Bao; Pan, Jeng-Shyang

2013-01-01

Kernel Learning Algorithms for Face Recognition covers the framework of kernel based face recognition. This book discusses the advanced kernel learning algorithms and its application on face recognition. This book also focuses on the theoretical deviation, the system framework and experiments involving kernel based face recognition. Included within are algorithms of kernel based face recognition, and also the feasibility of the kernel based face recognition method. This book provides researchers in pattern recognition and machine learning area with advanced face recognition methods and its new

Sudden Event Recognition: A Survey

Directory of Open Access Journals (Sweden)

Mohd Asyraf Zulkifley

2013-08-01

Full Text Available Event recognition is one of the most active research areas in video surveillance fields. Advancement in event recognition systems mainly aims to provide convenience, safety and an efficient lifestyle for humanity. A precise, accurate and robust approach is necessary to enable event recognition systems to respond to sudden changes in various uncontrolled environments, such as the case of an emergency, physical threat and a fire or bomb alert. The performance of sudden event recognition systems depends heavily on the accuracy of low level processing, like detection, recognition, tracking and machine learning algorithms. This survey aims to detect and characterize a sudden event, which is a subset of an abnormal event in several video surveillance applications. This paper discusses the following in detail: (1 the importance of a sudden event over a general anomalous event; (2 frameworks used in sudden event recognition; (3 the requirements and comparative studies of a sudden event recognition system and (4 various decision-making approaches for sudden event recognition. The advantages and drawbacks of using 3D images from multiple cameras for real-time application are also discussed. The paper concludes with suggestions for future research directions in sudden event recognition.

The Improvement of Behavior Recognition Accuracy of Micro Inertial Accelerometer by Secondary Recognition Algorithm

Directory of Open Access Journals (Sweden)

Yu Liu

2014-05-01

Full Text Available Behaviors of “still”, “walking”, “running”, “jumping”, “upstairs” and “downstairs” can be recognized by micro inertial accelerometer of low cost. By using the features as inputs to the well-trained BP artificial neural network which is selected as classifier, those behaviors can be recognized. But the experimental results show that the recognition accuracy is not satisfactory. This paper presents secondary recognition algorithm and combine it with BP artificial neural network to improving the recognition accuracy. The Algorithm is verified by the Android mobile platform, and the recognition accuracy can be improved more than 8 %. Through extensive testing statistic analysis, the recognition accuracy can reach 95 % through BP artificial neural network and the secondary recognition, which is a reasonable good result from practical point of view.

The Legal Recognition of Sign Languages

Science.gov (United States)

De Meulder, Maartje

2015-01-01

This article provides an analytical overview of the different types of explicit legal recognition of sign languages. Five categories are distinguished: constitutional recognition, recognition by means of general language legislation, recognition by means of a sign language law or act, recognition by means of a sign language law or act including…

Human activity recognition and prediction

CERN Document Server

2016-01-01

This book provides a unique view of human activity recognition, especially fine-grained human activity structure learning, human-interaction recognition, RGB-D data based action recognition, temporal decomposition, and causality learning in unconstrained human activity videos. The techniques discussed give readers tools that provide a significant improvement over existing methodologies of video content understanding by taking advantage of activity recognition. It links multiple popular research fields in computer vision, machine learning, human-centered computing, human-computer interaction, image classification, and pattern recognition. In addition, the book includes several key chapters covering multiple emerging topics in the field. Contributed by top experts and practitioners, the chapters present key topics from different angles and blend both methodology and application, composing a solid overview of the human activity recognition techniques. .

Radar automatic target recognition (ATR) and non-cooperative target recognition (NCTR)

CERN Document Server

Blacknell, David

2013-01-01

The ability to detect and locate targets by day or night, over wide areas, regardless of weather conditions has long made radar a key sensor in many military and civil applications. However, the ability to automatically and reliably distinguish different targets represents a difficult challenge. Radar Automatic Target Recognition (ATR) and Non-Cooperative Target Recognition (NCTR) captures material presented in the NATO SET-172 lecture series to provide an overview of the state-of-the-art and continuing challenges of radar target recognition. Topics covered include the problem as applied to th

Recognition and Toleration

DEFF Research Database (Denmark)

Lægaard, Sune

2010-01-01

Recognition and toleration are ways of relating to the diversity characteristic of multicultural societies. The article concerns the possible meanings of toleration and recognition, and the conflict that is often claimed to exist between these two approaches to diversity. Different forms or inter...

Super-recognition in development: A case study of an adolescent with extraordinary face recognition skills.

Science.gov (United States)

Bennetts, Rachel J; Mole, Joseph; Bate, Sarah

2017-09-01

Face recognition abilities vary widely. While face recognition deficits have been reported in children, it is unclear whether superior face recognition skills can be encountered during development. This paper presents O.B., a 14-year-old female with extraordinary face recognition skills: a "super-recognizer" (SR). O.B. demonstrated exceptional face-processing skills across multiple tasks, with a level of performance that is comparable to adult SRs. Her superior abilities appear to be specific to face identity: She showed an exaggerated face inversion effect and her superior abilities did not extend to object processing or non-identity aspects of face recognition. Finally, an eye-movement task demonstrated that O.B. spent more time than controls examining the nose - a pattern previously reported in adult SRs. O.B. is therefore particularly skilled at extracting and using identity-specific facial cues, indicating that face and object recognition are dissociable during development, and that super recognition can be detected in adolescence.

Within-individual variation in bullfrog vocalizations: implications for a vocally mediated social recognition system.

Science.gov (United States)

Bee, Mark A

2004-12-01

Acoustic signals provide a basis for social recognition in a wide range of animals. Few studies, however, have attempted to relate the patterns of individual variation in signals to behavioral discrimination thresholds used by receivers to discriminate among individuals. North American bullfrogs (Rana catesbeiana) discriminate among familiar and unfamiliar individuals based on individual variation in advertisement calls. The sources, patterns, and magnitudes of variation in eight acoustic properties of multiple-note advertisement calls were examined to understand how patterns of within-individual variation might either constrain, or provide additional cues for, vocal recognition. Six of eight acoustic properties exhibited significant note-to-note variation within multiple-note calls. Despite this source of within-individual variation, all call properties varied significantly among individuals, and multivariate analyses indicated that call notes were individually distinct. Fine-temporal and spectral call properties exhibited less within-individual variation compared to gross-temporal properties and contributed most toward statistically distinguishing among individuals. Among-individual differences in the patterns of within-individual variation in some properties suggest that within-individual variation could also function as a recognition cue. The distributions of among-individual and within-individual differences were used to generate hypotheses about the expected behavioral discrimination thresholds of receivers.

A motivational determinant of facial emotion recognition: regulatory focus affects recognition of emotions in faces.

Science.gov (United States)

Sassenrath, Claudia; Sassenberg, Kai; Ray, Devin G; Scheiter, Katharina; Jarodzka, Halszka

2014-01-01

Two studies examined an unexplored motivational determinant of facial emotion recognition: observer regulatory focus. It was predicted that a promotion focus would enhance facial emotion recognition relative to a prevention focus because the attentional strategies associated with promotion focus enhance performance on well-learned or innate tasks - such as facial emotion recognition. In Study 1, a promotion or a prevention focus was experimentally induced and better facial emotion recognition was observed in a promotion focus compared to a prevention focus. In Study 2, individual differences in chronic regulatory focus were assessed and attention allocation was measured using eye tracking during the facial emotion recognition task. Results indicated that the positive relation between a promotion focus and facial emotion recognition is mediated by shorter fixation duration on the face which reflects a pattern of attention allocation matched to the eager strategy in a promotion focus (i.e., striving to make hits). A prevention focus did not have an impact neither on perceptual processing nor on facial emotion recognition. Taken together, these findings demonstrate important mechanisms and consequences of observer motivational orientation for facial emotion recognition.

Pattern recognition & machine learning

CERN Document Server

Anzai, Y

1992-01-01

This is the first text to provide a unified and self-contained introduction to visual pattern recognition and machine learning. It is useful as a general introduction to artifical intelligence and knowledge engineering, and no previous knowledge of pattern recognition or machine learning is necessary. Basic for various pattern recognition and machine learning methods. Translated from Japanese, the book also features chapter exercises, keywords, and summaries.

A REVIEW: OPTICAL CHARACTER RECOGNITION

OpenAIRE

Swati Tomar*1 & Amit Kishore2

2018-01-01

This paper presents detailed review in the field of Optical Character Recognition. Various techniques are determine that have been proposed to realize the center of character recognition in an optical character recognition system. Even though, sufficient studies and papers are describes the techniques for converting textual content from a paper document into machine readable form. Optical character recognition is a process where the computer understands automatically the image of handwritten ...

Challenging ocular image recognition

Science.gov (United States)

Pauca, V. Paúl; Forkin, Michael; Xu, Xiao; Plemmons, Robert; Ross, Arun A.

2011-06-01

Ocular recognition is a new area of biometric investigation targeted at overcoming the limitations of iris recognition performance in the presence of non-ideal data. There are several advantages for increasing the area beyond the iris, yet there are also key issues that must be addressed such as size of the ocular region, factors affecting performance, and appropriate corpora to study these factors in isolation. In this paper, we explore and identify some of these issues with the goal of better defining parameters for ocular recognition. An empirical study is performed where iris recognition methods are contrasted with texture and point operators on existing iris and face datasets. The experimental results show a dramatic recognition performance gain when additional features are considered in the presence of poor quality iris data, offering strong evidence for extending interest beyond the iris. The experiments also highlight the need for the direct collection of additional ocular imagery.

Genetic specificity of face recognition.

Science.gov (United States)

Shakeshaft, Nicholas G; Plomin, Robert

2015-10-13

Specific cognitive abilities in diverse domains are typically found to be highly heritable and substantially correlated with general cognitive ability (g), both phenotypically and genetically. Recent twin studies have found the ability to memorize and recognize faces to be an exception, being similarly heritable but phenotypically substantially uncorrelated both with g and with general object recognition. However, the genetic relationships between face recognition and other abilities (the extent to which they share a common genetic etiology) cannot be determined from phenotypic associations. In this, to our knowledge, first study of the genetic associations between face recognition and other domains, 2,000 18- and 19-year-old United Kingdom twins completed tests assessing their face recognition, object recognition, and general cognitive abilities. Results confirmed the substantial heritability of face recognition (61%), and multivariate genetic analyses found that most of this genetic influence is unique and not shared with other cognitive abilities.

Face Detection and Recognition

National Research Council Canada - National Science Library

Jain, Anil K

2004-01-01

This report describes research efforts towards developing algorithms for a robust face recognition system to overcome many of the limitations found in existing two-dimensional facial recognition systems...

Forensic Face Recognition: A Survey

NARCIS (Netherlands)

Ali, Tauseef; Spreeuwers, Lieuwe Jan; Veldhuis, Raymond N.J.; Quaglia, Adamo; Epifano, Calogera M.

2012-01-01

The improvements of automatic face recognition during the last 2 decades have disclosed new applications like border control and camera surveillance. A new application field is forensic face recognition. Traditionally, face recognition by human experts has been used in forensics, but now there is a

Voice Recognition in Face-Blind Patients

Science.gov (United States)

Liu, Ran R.; Pancaroglu, Raika; Hills, Charlotte S.; Duchaine, Brad; Barton, Jason J. S.

2016-01-01

Right or bilateral anterior temporal damage can impair face recognition, but whether this is an associative variant of prosopagnosia or part of a multimodal disorder of person recognition is an unsettled question, with implications for cognitive and neuroanatomic models of person recognition. We assessed voice perception and short-term recognition of recently heard voices in 10 subjects with impaired face recognition acquired after cerebral lesions. All 4 subjects with apperceptive prosopagnosia due to lesions limited to fusiform cortex had intact voice discrimination and recognition. One subject with bilateral fusiform and anterior temporal lesions had a combined apperceptive prosopagnosia and apperceptive phonagnosia, the first such described case. Deficits indicating a multimodal syndrome of person recognition were found only in 2 subjects with bilateral anterior temporal lesions. All 3 subjects with right anterior temporal lesions had normal voice perception and recognition, 2 of whom performed normally on perceptual discrimination of faces. This confirms that such lesions can cause a modality-specific associative prosopagnosia. PMID:25349193

Invariant Face recognition Using Infrared Images

International Nuclear Information System (INIS)

Zahran, E.G.

2012-01-01

Over the past few decades, face recognition has become a rapidly growing research topic due to the increasing demands in many applications of our daily life such as airport surveillance, personal identification in law enforcement, surveillance systems, information safety, securing financial transactions, and computer security. The objective of this thesis is to develop a face recognition system capable of recognizing persons with a high recognition capability, low processing time, and under different illumination conditions, and different facial expressions. The thesis presents a study for the performance of the face recognition system using two techniques; the Principal Component Analysis (PCA), and the Zernike Moments (ZM). The performance of the recognition system is evaluated according to several aspects including the recognition rate, and the processing time. Face recognition systems that use visual images are sensitive to variations in the lighting conditions and facial expressions. The performance of these systems may be degraded under poor illumination conditions or for subjects of various skin colors. Several solutions have been proposed to overcome these limitations. One of these solutions is to work in the Infrared (IR) spectrum. IR images have been suggested as an alternative source of information for detection and recognition of faces, when there is little or no control over lighting conditions. This arises from the fact that these images are formed due to thermal emissions from skin, which is an intrinsic property because these emissions depend on the distribution of blood vessels under the skin. On the other hand IR face recognition systems still have limitations with temperature variations and recognition of persons wearing eye glasses. In this thesis we will fuse IR images with visible images to enhance the performance of face recognition systems. Images are fused using the wavelet transform. Simulation results show that the fusion of visible and

End-Stop Exemplar Based Recognition

DEFF Research Database (Denmark)

Olsen, Søren I.

2003-01-01

An approach to exemplar based recognition of visual shapes is presented. The shape information is described by attributed interest points (keys) detected by an end-stop operator. The attributes describe the statistics of lines and edges local to the interest point, the position of neighboring int...... interest points, and (in the training phase) a list of recognition names. Recognition is made by a simple voting procedure. Preliminary experiments indicate that the recognition is robust to noise, small deformations, background clutter and partial occlusion....

Speech Recognition on Mobile Devices

DEFF Research Database (Denmark)

Tan, Zheng-Hua; Lindberg, Børge

2010-01-01

in the mobile context covering motivations, challenges, fundamental techniques and applications. Three ASR architectures are introduced: embedded speech recognition, distributed speech recognition and network speech recognition. Their pros and cons and implementation issues are discussed. Applications within......The enthusiasm of deploying automatic speech recognition (ASR) on mobile devices is driven both by remarkable advances in ASR technology and by the demand for efficient user interfaces on such devices as mobile phones and personal digital assistants (PDAs). This chapter presents an overview of ASR...

Markov Models for Handwriting Recognition

CERN Document Server

Plotz, Thomas

2011-01-01

Since their first inception, automatic reading systems have evolved substantially, yet the recognition of handwriting remains an open research problem due to its substantial variation in appearance. With the introduction of Markovian models to the field, a promising modeling and recognition paradigm was established for automatic handwriting recognition. However, no standard procedures for building Markov model-based recognizers have yet been established. This text provides a comprehensive overview of the application of Markov models in the field of handwriting recognition, covering both hidden

Examining ERP correlates of recognition memory: Evidence of accurate source recognition without recollection

Science.gov (United States)

Addante, Richard, J.; Ranganath, Charan; Yonelinas, Andrew, P.

2012-01-01

Recollection is typically associated with high recognition confidence and accurate source memory. However, subjects sometimes make accurate source memory judgments even for items that are not confidently recognized, and it is not known whether these responses are based on recollection or some other memory process. In the current study, we measured event related potentials (ERPs) while subjects made item and source memory confidence judgments in order to determine whether recollection supported accurate source recognition responses for items that were not confidently recognized. In line with previous studies, we found that recognition memory was associated with two ERP effects: an early on-setting FN400 effect, and a later parietal old-new effect [Late Positive Component (LPC)], which have been associated with familiarity and recollection, respectively. The FN400 increased gradually with item recognition confidence, whereas the LPC was only observed for highly confident recognition responses. The LPC was also related to source accuracy, but only for items that had received a high confidence item recognition response; accurate source judgments to items that were less confidently recognized did not exhibit the typical ERP correlate of recollection or familiarity, but rather showed a late, broadly distributed negative ERP difference. The results indicate that accurate source judgments of episodic context can occur even when recollection fails. PMID:22548808

Evaluating music emotion recognition:Lessons from music genre recognition?

OpenAIRE

Sturm, Bob L.

2013-01-01

A fundamental problem with nearly all work in music genre recognition (MGR)is that evaluation lacks validity with respect to the principal goals of MGR. This problem also occurs in the evaluation of music emotion recognition (MER). Standard approaches to evaluation, though easy to implement, do not reliably differentiate between recognizing genre or emotion from music, or by virtue of confounding factors in signals (e.g., equalization). We demonstrate such problems for evaluating an MER syste...

Word Recognition in Auditory Cortex

Science.gov (United States)

DeWitt, Iain D. J.

2013-01-01

Although spoken word recognition is more fundamental to human communication than text recognition, knowledge of word-processing in auditory cortex is comparatively impoverished. This dissertation synthesizes current models of auditory cortex, models of cortical pattern recognition, models of single-word reading, results in phonetics and results in…

A marketing science perspective on recognition-based heuristics (and the fast-and-frugal paradigm

Directory of Open Access Journals (Sweden)

John Hauser

2011-07-01

Full Text Available Marketing science seeks to prescribe better marketing strategies (advertising, product development, pricing, etc.. To do so we rely on models of consumer decisions grounded in empirical observations. Field experience suggests that recognition-based heuristics help consumers to choose which brands to consider and purchase in frequently-purchased categories, but other heuristics are more relevant in durable-goods categories. Screening with recognition is a rational screening rule when advertising is a signal of product quality, when observing other consumers makes it easy to learn decision rules, and when firms react to engineering-design constraints by offering brands such that a high-level on one product feature implies a low level on another product feature. Experience with applications and field experiments suggests four fruitful research topics: deciding how to decide (endogeneity, learning decision rules by self-reflection, risk reduction, and the difference between utility functions and decision rules. These challenges also pose methodological cautions.

[Comparative studies of face recognition].

Science.gov (United States)

Kawai, Nobuyuki

2012-07-01

Every human being is proficient in face recognition. However, the reason for and the manner in which humans have attained such an ability remain unknown. These questions can be best answered-through comparative studies of face recognition in non-human animals. Studies in both primates and non-primates show that not only primates, but also non-primates possess the ability to extract information from their conspecifics and from human experimenters. Neural specialization for face recognition is shared with mammals in distant taxa, suggesting that face recognition evolved earlier than the emergence of mammals. A recent study indicated that a social insect, the golden paper wasp, can distinguish their conspecific faces, whereas a closely related species, which has a less complex social lifestyle with just one queen ruling a nest of underlings, did not show strong face recognition for their conspecifics. Social complexity and the need to differentiate between one another likely led humans to evolve their face recognition abilities.

Is having similar eye movement patterns during face learning and recognition beneficial for recognition performance? Evidence from hidden Markov modeling.

Science.gov (United States)

Chuk, Tim; Chan, Antoni B; Hsiao, Janet H

2017-12-01

The hidden Markov model (HMM)-based approach for eye movement analysis is able to reflect individual differences in both spatial and temporal aspects of eye movements. Here we used this approach to understand the relationship between eye movements during face learning and recognition, and its association with recognition performance. We discovered holistic (i.e., mainly looking at the face center) and analytic (i.e., specifically looking at the two eyes in addition to the face center) patterns during both learning and recognition. Although for both learning and recognition, participants who adopted analytic patterns had better recognition performance than those with holistic patterns, a significant positive correlation between the likelihood of participants' patterns being classified as analytic and their recognition performance was only observed during recognition. Significantly more participants adopted holistic patterns during learning than recognition. Interestingly, about 40% of the participants used different patterns between learning and recognition, and among them 90% switched their patterns from holistic at learning to analytic at recognition. In contrast to the scan path theory, which posits that eye movements during learning have to be recapitulated during recognition for the recognition to be successful, participants who used the same or different patterns during learning and recognition did not differ in recognition performance. The similarity between their learning and recognition eye movement patterns also did not correlate with their recognition performance. These findings suggested that perceptuomotor memory elicited by eye movement patterns during learning does not play an important role in recognition. In contrast, the retrieval of diagnostic information for recognition, such as the eyes for face recognition, is a better predictor for recognition performance. Copyright © 2017 Elsevier Ltd. All rights reserved.

Body shape-based biometric recognition using millimeter wave images

OpenAIRE

González-Sosa, Ester; Vera-Rodríguez, Rubén; Fiérrez, Julián; Ortega-García, Javier

2013-01-01

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. González-Sosa, E. ; Vera-Rodríguez, R. ; Fierrez, J. ; Ortega-García, J. "Body shape-based biometric recognition using millime...

Visual Recognition Memory across Contexts

Science.gov (United States)

Jones, Emily J. H.; Pascalis, Olivier; Eacott, Madeline J.; Herbert, Jane S.

2011-01-01

In two experiments, we investigated the development of representational flexibility in visual recognition memory during infancy using the Visual Paired Comparison (VPC) task. In Experiment 1, 6- and 9-month-old infants exhibited recognition when familiarization and test occurred in the same room, but showed no evidence of recognition when…

Non Audio-Video gesture recognition system

DEFF Research Database (Denmark)

Craciunescu, Razvan; Mihovska, Albena Dimitrova; Kyriazakos, Sofoklis

2016-01-01

Gesture recognition is a topic in computer science and language technology with the goal of interpreting human gestures via mathematical algorithms. Gestures can originate from any bodily motion or state but commonly originate from the face or hand. Current research focus includes on the emotion...... recognition from the face and hand gesture recognition. Gesture recognition enables humans to communicate with the machine and interact naturally without any mechanical devices. This paper investigates the possibility to use non-audio/video sensors in order to design a low-cost gesture recognition device...

Children's recognition of advertisements on television and on Web pages.

Science.gov (United States)

Blades, Mark; Oates, Caroline; Li, Shiying

2013-03-01

In this paper we consider the issue of advertising to children. Advertising to children raises a number of concerns, in particular the effects of food advertising on children's eating habits. We point out that virtually all the research into children's understanding of advertising has focused on traditional television advertisements, but much marketing aimed at children is now via the Internet and little is known about children's awareness of advertising on the Web. One important component of understanding advertisements is the ability to distinguish advertisements from other messages, and we suggest that young children's ability to recognise advertisements on a Web page is far behind their ability to recognise advertisements on television. Copyright © 2012 Elsevier Ltd. All rights reserved.

Comparing Face Detection and Recognition Techniques

OpenAIRE

Korra, Jyothi

2016-01-01

This paper implements and compares different techniques for face detection and recognition. One is find where the face is located in the images that is face detection and second is face recognition that is identifying the person. We study three techniques in this paper: Face detection using self organizing map (SOM), Face recognition by projection and nearest neighbor and Face recognition using SVM.

Exemplar Based Recognition of Visual Shapes

DEFF Research Database (Denmark)

Olsen, Søren I.

2005-01-01

This paper presents an approach of visual shape recognition based on exemplars of attributed keypoints. Training is performed by storing exemplars of keypoints detected in labeled training images. Recognition is made by keypoint matching and voting according to the labels for the matched keypoint....... The matching is insensitive to rotations, limited scalings and small deformations. The recognition is robust to noise, background clutter and partial occlusion. Recognition is possible from few training images and improve with the number of training images.......This paper presents an approach of visual shape recognition based on exemplars of attributed keypoints. Training is performed by storing exemplars of keypoints detected in labeled training images. Recognition is made by keypoint matching and voting according to the labels for the matched keypoints...

Superficial Priming in Episodic Recognition

Science.gov (United States)

Dopkins, Stephen; Sargent, Jesse; Ngo, Catherine T.

2010-01-01

We explored the effect of superficial priming in episodic recognition and found it to be different from the effect of semantic priming in episodic recognition. Participants made recognition judgments to pairs of items, with each pair consisting of a prime item and a test item. Correct positive responses to the test item were impeded if the prime…

Specification for projects of radiogeologic recognition

International Nuclear Information System (INIS)

1979-01-01

This instruction is a guidance to achievement of radiogeologic recognition projects. The radiogeologic recognition is a prospecting method that join the classic geologic recognition with measures of rock radioactivity. (C.M.)

Why recognition is rational

Directory of Open Access Journals (Sweden)

Clintin P. Davis-Stober

2010-07-01

Full Text Available The Recognition Heuristic (Gigerenzer and Goldstein, 1996; Goldstein and Gigerenzer, 2002 makes the counter-intuitive prediction that a decision maker utilizing less information may do as well as, or outperform, an idealized decision maker utilizing more information. We lay a theoretical foundation for the use of single-variable heuristics such as the Recognition Heuristic as an optimal decision strategy within a linear modeling framework. We identify conditions under which over-weighting a single predictor is a mini-max strategy among a class of a priori chosen weights based on decision heuristics with respect to a measure of statistical lack of fit we call ``risk''. These strategies, in turn, outperform standard multiple regression as long as the amount of data available is limited. We also show that, under related conditions, weighting only one variable and ignoring all others produces the same risk as ignoring the single variable and weighting all others. This approach has the advantage of generalizing beyond the original environment of the Recognition Heuristic to situations with more than two choice options, binary or continuous representations of recognition, and to other single variable heuristics. We analyze the structure of data used in some prior recognition tasks and find that it matches the sufficient conditions for optimality in our results. Rather than being a poor or adequate substitute for a compensatory model, the Recognition Heuristic closely approximates an optimal strategy when a decision maker has finite data about the world.

Infant visual attention and object recognition.

Science.gov (United States)

Reynolds, Greg D

2015-05-15

This paper explores the role visual attention plays in the recognition of objects in infancy. Research and theory on the development of infant attention and recognition memory are reviewed in three major sections. The first section reviews some of the major findings and theory emerging from a rich tradition of behavioral research utilizing preferential looking tasks to examine visual attention and recognition memory in infancy. The second section examines research utilizing neural measures of attention and object recognition in infancy as well as research on brain-behavior relations in the early development of attention and recognition memory. The third section addresses potential areas of the brain involved in infant object recognition and visual attention. An integrated synthesis of some of the existing models of the development of visual attention is presented which may account for the observed changes in behavioral and neural measures of visual attention and object recognition that occur across infancy. Copyright © 2015 Elsevier B.V. All rights reserved.

Split-second recognition: what makes outdoor advertising work?

NARCIS (Netherlands)

van Meurs, A.; Aristoff, M.

2009-01-01

CBS Outdoor used a tachistoscope to determine how long it takes to recognize the brand/product advertised in 187 outdoor posters in the Netherlands. Additionally, CBS Outdoor measured the creative appeal of these advertisements. Using 80 content and format variables, an explanatory model was

Probabilistic Open Set Recognition

Science.gov (United States)

Jain, Lalit Prithviraj

Real-world tasks in computer vision, pattern recognition and machine learning often touch upon the open set recognition problem: multi-class recognition with incomplete knowledge of the world and many unknown inputs. An obvious way to approach such problems is to develop a recognition system that thresholds probabilities to reject unknown classes. Traditional rejection techniques are not about the unknown; they are about the uncertain boundary and rejection around that boundary. Thus traditional techniques only represent the "known unknowns". However, a proper open set recognition algorithm is needed to reduce the risk from the "unknown unknowns". This dissertation examines this concept and finds existing probabilistic multi-class recognition approaches are ineffective for true open set recognition. We hypothesize the cause is due to weak adhoc assumptions combined with closed-world assumptions made by existing calibration techniques. Intuitively, if we could accurately model just the positive data for any known class without overfitting, we could reject the large set of unknown classes even under this assumption of incomplete class knowledge. For this, we formulate the problem as one of modeling positive training data by invoking statistical extreme value theory (EVT) near the decision boundary of positive data with respect to negative data. We provide a new algorithm called the PI-SVM for estimating the unnormalized posterior probability of class inclusion. This dissertation also introduces a new open set recognition model called Compact Abating Probability (CAP), where the probability of class membership decreases in value (abates) as points move from known data toward open space. We show that CAP models improve open set recognition for multiple algorithms. Leveraging the CAP formulation, we go on to describe the novel Weibull-calibrated SVM (W-SVM) algorithm, which combines the useful properties of statistical EVT for score calibration with one-class and binary

Hemispheric lateralization of linguistic prosody recognition in comparison to speech and speaker recognition.

Science.gov (United States)

Kreitewolf, Jens; Friederici, Angela D; von Kriegstein, Katharina

2014-11-15

Hemispheric specialization for linguistic prosody is a controversial issue. While it is commonly assumed that linguistic prosody and emotional prosody are preferentially processed in the right hemisphere, neuropsychological work directly comparing processes of linguistic prosody and emotional prosody suggests a predominant role of the left hemisphere for linguistic prosody processing. Here, we used two functional magnetic resonance imaging (fMRI) experiments to clarify the role of left and right hemispheres in the neural processing of linguistic prosody. In the first experiment, we sought to confirm previous findings showing that linguistic prosody processing compared to other speech-related processes predominantly involves the right hemisphere. Unlike previous studies, we controlled for stimulus influences by employing a prosody and speech task using the same speech material. The second experiment was designed to investigate whether a left-hemispheric involvement in linguistic prosody processing is specific to contrasts between linguistic prosody and emotional prosody or whether it also occurs when linguistic prosody is contrasted against other non-linguistic processes (i.e., speaker recognition). Prosody and speaker tasks were performed on the same stimulus material. In both experiments, linguistic prosody processing was associated with activity in temporal, frontal, parietal and cerebellar regions. Activation in temporo-frontal regions showed differential lateralization depending on whether the control task required recognition of speech or speaker: recognition of linguistic prosody predominantly involved right temporo-frontal areas when it was contrasted against speech recognition; when contrasted against speaker recognition, recognition of linguistic prosody predominantly involved left temporo-frontal areas. The results show that linguistic prosody processing involves functions of both hemispheres and suggest that recognition of linguistic prosody is based on

Viewpoint Manifolds for Action Recognition

Directory of Open Access Journals (Sweden)

Souvenir Richard

2009-01-01

Full Text Available Abstract Action recognition from video is a problem that has many important applications to human motion analysis. In real-world settings, the viewpoint of the camera cannot always be fixed relative to the subject, so view-invariant action recognition methods are needed. Previous view-invariant methods use multiple cameras in both the training and testing phases of action recognition or require storing many examples of a single action from multiple viewpoints. In this paper, we present a framework for learning a compact representation of primitive actions (e.g., walk, punch, kick, sit that can be used for video obtained from a single camera for simultaneous action recognition and viewpoint estimation. Using our method, which models the low-dimensional structure of these actions relative to viewpoint, we show recognition rates on a publicly available dataset previously only achieved using multiple simultaneous views.

Physiological arousal in processing recognition information

Directory of Open Access Journals (Sweden)

Guy Hochman

2010-07-01

Full Text Available The recognition heuristic (RH; Goldstein and Gigerenzer, 2002 suggests that, when applicable, probabilistic inferences are based on a noncompensatory examination of whether an object is recognized or not. The overall findings on the processes that underlie this fast and frugal heuristic are somewhat mixed, and many studies have expressed the need for considering a more compensatory integration of recognition information. Regardless of the mechanism involved, it is clear that recognition has a strong influence on choices, and this finding might be explained by the fact that recognition cues arouse affect and thus receive more attention than cognitive cues. To test this assumption, we investigated whether recognition results in a direct affective signal by measuring physiological arousal (i.e., peripheral arterial tone in the established city-size task. We found that recognition of cities does not directly result in increased physiological arousal. Moreover, the results show that physiological arousal increased with increasing inconsistency between recognition information and additional cue information. These findings support predictions derived by a compensatory Parallel Constraint Satisfaction model rather than predictions of noncompensatory models. Additional results concerning confidence ratings, response times, and choice proportions further demonstrated that recognition information and other cognitive cues are integrated in a compensatory manner.

Page Recognition: Quantum Leap In Recognition Technology

Science.gov (United States)

Miller, Larry

1989-07-01

No milestone has proven as elusive as the always-approaching "year of the LAN," but the "year of the scanner" might claim the silver medal. Desktop scanners have been around almost as long as personal computers. And everyone thinks they are used for obvious desktop-publishing and business tasks like scanning business documents, magazine articles and other pages, and translating those words into files your computer understands. But, until now, the reality fell far short of the promise. Because it's true that scanners deliver an accurate image of the page to your computer, but the software to recognize this text has been woefully disappointing. Old optical-character recognition (OCR) software recognized such a limited range of pages as to be virtually useless to real users. (For example, one OCR vendor specified 12-point Courier font from an IBM Selectric typewriter: the same font in 10-point, or from a Diablo printer, was unrecognizable!) Computer dealers have told me the chasm between OCR expectations and reality is so broad and deep that nine out of ten prospects leave their stores in disgust when they learn the limitations. And this is a very important, very unfortunate gap. Because the promise of recognition -- what people want it to do -- carries with it tremendous improvements in our productivity and ability to get tons of written documents into our computers where we can do real work with it. The good news is that a revolutionary new development effort has led to the new technology of "page recognition," which actually does deliver the promise we've always wanted from OCR. I'm sure every reader appreciates the breakthrough represented by the laser printer and page-makeup software, a combination so powerful it created new reasons for buying a computer. A similar breakthrough is happening right now in page recognition: the Macintosh (and, I must admit, other personal computers) equipped with a moderately priced scanner and OmniPage software (from Caere

Online handwritten mathematical expression recognition

Science.gov (United States)

Büyükbayrak, Hakan; Yanikoglu, Berrin; Erçil, Aytül

2007-01-01

We describe a system for recognizing online, handwritten mathematical expressions. The system is designed with a user-interface for writing scientific articles, supporting the recognition of basic mathematical expressions as well as integrals, summations, matrices etc. A feed-forward neural network recognizes symbols which are assumed to be single-stroke and a recursive algorithm parses the expression by combining neural network output and the structure of the expression. Preliminary results show that writer-dependent recognition rates are very high (99.8%) while writer-independent symbol recognition rates are lower (75%). The interface associated with the proposed system integrates the built-in recognition capabilities of the Microsoft's Tablet PC API for recognizing textual input and supports conversion of hand-drawn figures into PNG format. This enables the user to enter text, mathematics and draw figures in a single interface. After recognition, all output is combined into one LATEX code and compiled into a PDF file.

Side-View Face Recognition

NARCIS (Netherlands)

Santemiz, P.; Spreeuwers, Lieuwe Jan; Veldhuis, Raymond N.J.; van den Biggelaar, Olivier

As a widely used biometrics, face recognition has many advantages such as being non-intrusive, natural and passive. On the other hand, in real-life scenarios with uncontrolled environment, pose variation up to side-view positions makes face recognition a challenging work. In this paper we discuss

[Neurological disease and facial recognition].

Science.gov (United States)

Kawamura, Mitsuru; Sugimoto, Azusa; Kobayakawa, Mutsutaka; Tsuruya, Natsuko

2012-07-01

To discuss the neurological basis of facial recognition, we present our case reports of impaired recognition and a review of previous literature. First, we present a case of infarction and discuss prosopagnosia, which has had a large impact on face recognition research. From a study of patient symptoms, we assume that prosopagnosia may be caused by unilateral right occipitotemporal lesion and right cerebral dominance of facial recognition. Further, circumscribed lesion and degenerative disease may also cause progressive prosopagnosia. Apperceptive prosopagnosia is observed in patients with posterior cortical atrophy (PCA), pathologically considered as Alzheimer's disease, and associative prosopagnosia in frontotemporal lobar degeneration (FTLD). Second, we discuss face recognition as part of communication. Patients with Parkinson disease show social cognitive impairments, such as difficulty in facial expression recognition and deficits in theory of mind as detected by the reading the mind in the eyes test. Pathological and functional imaging studies indicate that social cognitive impairment in Parkinson disease is possibly related to damages in the amygdalae and surrounding limbic system. The social cognitive deficits can be observed in the early stages of Parkinson disease, and even in the prodromal stage, for example, patients with rapid eye movement (REM) sleep behavior disorder (RBD) show impairment in facial expression recognition. Further, patients with myotonic dystrophy type 1 (DM 1), which is a multisystem disease that mainly affects the muscles, show social cognitive impairment similar to that of Parkinson disease. Our previous study showed that facial expression recognition impairment of DM 1 patients is associated with lesion in the amygdalae and insulae. Our study results indicate that behaviors and personality traits in DM 1 patients, which are revealed by social cognitive impairment, are attributable to dysfunction of the limbic system.

Recognition of an Independent Self-Consciousness

DEFF Research Database (Denmark)

Bjerre, Henrik Jøker

2009-01-01

Hegel's concept in the Phenomenology of the Spirit of the "recognition of an independent self-consciousness" is investigated as a point of separation for contemporary philosophy of recognition. I claim that multiculturalism and the theories of recognition (such as Axel Honneth's) based on empiric...... psychology neglect or deny crucial metaphysical aspects of the Hegelian legacy. Instead, I seek to point at an additional, "spiritual", level of recognition, based on the concept of the subject in Lacanian psychoanalysis....

Case-Based Policy and Goal Recognition

Science.gov (United States)

2015-09-30

Policy and Goal Recognizer (PaGR), a case- based system for multiagent keyhole recognition. PaGR is a knowledge recognition component within a decision...However, unlike our agent in the BVR domain, these recognition agents have access to perfect information. Single-agent keyhole plan recognition can be...listed below: 1. Facing Target 2. Closing on Target 3. Target Range 4. Within a Target’s Weapon Range 5. Has Target within Weapon Range 6. Is in Danger

Harmonization versus Mutual Recognition

DEFF Research Database (Denmark)

Jørgensen, Jan Guldager; Schröder, Philipp

The present paper examines trade liberalization driven by the coordination of product standards. For oligopolistic firms situated in separate markets that are initially sheltered by national standards, mutual recognition of standards implies entry and reduced profits at home paired with the oppor......The present paper examines trade liberalization driven by the coordination of product standards. For oligopolistic firms situated in separate markets that are initially sheltered by national standards, mutual recognition of standards implies entry and reduced profits at home paired...... countries and three firms, where firms first lobby for the policy coordination regime (harmonization versus mutual recognition), and subsequently, in case of harmonization, the global standard is auctioned among the firms. We discuss welfare effects and conclude with policy implications. In particular......, harmonized standards may fail to harvest the full pro-competitive effects from trade liberalization compared to mutual recognition; moreover, the issue is most pronounced in markets featuring price competition....

State Toleration, Religious Recognition and Equality

DEFF Research Database (Denmark)

Lægaard, Sune

2013-01-01

In debates about multiculturalism, it is widely claimed that ‘toleration is not enough’ and that we need to go ‘beyond toleration’ to some form of politics of recognition in order to satisfactorily address contemporary forms of cultural diversity (e.g. the presence in Europe of Muslim minorities...... a conceptual question of whether the relation between states and minorities can be categoriseized in terms of recognition or toleration, but about a normative question of whether and how toleration and recognition secures equality. When toleration is inadequate, this is often because it institutionaliseizes...... and upholds specific inequalities. But politics of recognition may equally well institute inequalities, and in such cases unequal recognition may not be preferable to toleration....

Vision-Based Navigation and Recognition

National Research Council Canada - National Science Library

Rosenfeld, Azriel

1998-01-01

.... (4) Invariants: both geometric and other types. (5) Human faces: Analysis of images of human faces, including feature extraction, face recognition, compression, and recognition of facial expressions...

Vision-Based Navigation and Recognition

National Research Council Canada - National Science Library

Rosenfeld, Azriel

1996-01-01

.... (4) Invariants -- both geometric and other types. (5) Human faces: Analysis of images of human faces, including feature extraction, face recognition, compression, and recognition of facial expressions...

Voice congruency facilitates word recognition.

Science.gov (United States)

Campeanu, Sandra; Craik, Fergus I M; Alain, Claude

2013-01-01

Behavioral studies of spoken word memory have shown that context congruency facilitates both word and source recognition, though the level at which context exerts its influence remains equivocal. We measured event-related potentials (ERPs) while participants performed both types of recognition task with words spoken in four voices. Two voice parameters (i.e., gender and accent) varied between speakers, with the possibility that none, one or two of these parameters was congruent between study and test. Results indicated that reinstating the study voice at test facilitated both word and source recognition, compared to similar or no context congruency at test. Behavioral effects were paralleled by two ERP modulations. First, in the word recognition test, the left parietal old/new effect showed a positive deflection reflective of context congruency between study and test words. Namely, the same speaker condition provided the most positive deflection of all correctly identified old words. In the source recognition test, a right frontal positivity was found for the same speaker condition compared to the different speaker conditions, regardless of response success. Taken together, the results of this study suggest that the benefit of context congruency is reflected behaviorally and in ERP modulations traditionally associated with recognition memory.

Voice congruency facilitates word recognition.

Directory of Open Access Journals (Sweden)

Sandra Campeanu

Full Text Available Behavioral studies of spoken word memory have shown that context congruency facilitates both word and source recognition, though the level at which context exerts its influence remains equivocal. We measured event-related potentials (ERPs while participants performed both types of recognition task with words spoken in four voices. Two voice parameters (i.e., gender and accent varied between speakers, with the possibility that none, one or two of these parameters was congruent between study and test. Results indicated that reinstating the study voice at test facilitated both word and source recognition, compared to similar or no context congruency at test. Behavioral effects were paralleled by two ERP modulations. First, in the word recognition test, the left parietal old/new effect showed a positive deflection reflective of context congruency between study and test words. Namely, the same speaker condition provided the most positive deflection of all correctly identified old words. In the source recognition test, a right frontal positivity was found for the same speaker condition compared to the different speaker conditions, regardless of response success. Taken together, the results of this study suggest that the benefit of context congruency is reflected behaviorally and in ERP modulations traditionally associated with recognition memory.

Side-View Face Recognition

NARCIS (Netherlands)

Santemiz, P.; Spreeuwers, Lieuwe Jan; Veldhuis, Raymond N.J.

2010-01-01

Side-view face recognition is a challenging problem with many applications. Especially in real-life scenarios where the environment is uncontrolled, coping with pose variations up to side-view positions is an important task for face recognition. In this paper we discuss the use of side view face

Document recognition serving people with disabilities

Science.gov (United States)

Fruchterman, James R.

2007-01-01

Document recognition advances have improved the lives of people with print disabilities, by providing accessible documents. This invited paper provides perspectives on the author's career progression from document recognition professional to social entrepreneur applying this technology to help people with disabilities. Starting with initial thoughts about optical character recognition in college, it continues with the creation of accurate omnifont character recognition that did not require training. It was difficult to make a reading machine for the blind in a commercial setting, which led to the creation of a nonprofit social enterprise to deliver these devices around the world. This network of people with disabilities scanning books drove the creation of Bookshare.org, an online library of scanned books. Looking forward, the needs for improved document recognition technology to further lower the barriers to reading are discussed. Document recognition professionals should be proud of the positive impact their work has had on some of society's most disadvantaged communities.

Infant Visual Recognition Memory

Science.gov (United States)

Rose, Susan A.; Feldman, Judith F.; Jankowski, Jeffery J.

2004-01-01

Visual recognition memory is a robust form of memory that is evident from early infancy, shows pronounced developmental change, and is influenced by many of the same factors that affect adult memory; it is surprisingly resistant to decay and interference. Infant visual recognition memory shows (a) modest reliability, (b) good discriminant…

Similarity measures for face recognition

CERN Document Server

Vezzetti, Enrico

2015-01-01

Face recognition has several applications, including security, such as (authentication and identification of device users and criminal suspects), and in medicine (corrective surgery and diagnosis). Facial recognition programs rely on algorithms that can compare and compute the similarity between two sets of images. This eBook explains some of the similarity measures used in facial recognition systems in a single volume. Readers will learn about various measures including Minkowski distances, Mahalanobis distances, Hansdorff distances, cosine-based distances, among other methods. The book also summarizes errors that may occur in face recognition methods. Computer scientists "facing face" and looking to select and test different methods of computing similarities will benefit from this book. The book is also useful tool for students undertaking computer vision courses.

Bidirectional Modulation of Recognition Memory.

Science.gov (United States)

Ho, Jonathan W; Poeta, Devon L; Jacobson, Tara K; Zolnik, Timothy A; Neske, Garrett T; Connors, Barry W; Burwell, Rebecca D

2015-09-30

Perirhinal cortex (PER) has a well established role in the familiarity-based recognition of individual items and objects. For example, animals and humans with perirhinal damage are unable to distinguish familiar from novel objects in recognition memory tasks. In the normal brain, perirhinal neurons respond to novelty and familiarity by increasing or decreasing firing rates. Recent work also implicates oscillatory activity in the low-beta and low-gamma frequency bands in sensory detection, perception, and recognition. Using optogenetic methods in a spontaneous object exploration (SOR) task, we altered recognition memory performance in rats. In the SOR task, normal rats preferentially explore novel images over familiar ones. We modulated exploratory behavior in this task by optically stimulating channelrhodopsin-expressing perirhinal neurons at various frequencies while rats looked at novel or familiar 2D images. Stimulation at 30-40 Hz during looking caused rats to treat a familiar image as if it were novel by increasing time looking at the image. Stimulation at 30-40 Hz was not effective in increasing exploration of novel images. Stimulation at 10-15 Hz caused animals to treat a novel image as familiar by decreasing time looking at the image, but did not affect looking times for images that were already familiar. We conclude that optical stimulation of PER at different frequencies can alter visual recognition memory bidirectionally. Significance statement: Recognition of novelty and familiarity are important for learning, memory, and decision making. Perirhinal cortex (PER) has a well established role in the familiarity-based recognition of individual items and objects, but how novelty and familiarity are encoded and transmitted in the brain is not known. Perirhinal neurons respond to novelty and familiarity by changing firing rates, but recent work suggests that brain oscillations may also be important for recognition. In this study, we showed that stimulation of

Famous face recognition, face matching, and extraversion.

Science.gov (United States)

Lander, Karen; Poyarekar, Siddhi

2015-01-01

It has been previously established that extraverts who are skilled at interpersonal interaction perform significantly better than introverts on a face-specific recognition memory task. In our experiment we further investigate the relationship between extraversion and face recognition, focusing on famous face recognition and face matching. Results indicate that more extraverted individuals perform significantly better on an upright famous face recognition task and show significantly larger face inversion effects. However, our results did not find an effect of extraversion on face matching or inverted famous face recognition.

Kazakh Traditional Dance Gesture Recognition

Science.gov (United States)

Nussipbekov, A. K.; Amirgaliyev, E. N.; Hahn, Minsoo

2014-04-01

Full body gesture recognition is an important and interdisciplinary research field which is widely used in many application spheres including dance gesture recognition. The rapid growth of technology in recent years brought a lot of contribution in this domain. However it is still challenging task. In this paper we implement Kazakh traditional dance gesture recognition. We use Microsoft Kinect camera to obtain human skeleton and depth information. Then we apply tree-structured Bayesian network and Expectation Maximization algorithm with K-means clustering to calculate conditional linear Gaussians for classifying poses. And finally we use Hidden Markov Model to detect dance gestures. Our main contribution is that we extend Kinect skeleton by adding headwear as a new skeleton joint which is calculated from depth image. This novelty allows us to significantly improve the accuracy of head gesture recognition of a dancer which in turn plays considerable role in whole body gesture recognition. Experimental results show the efficiency of the proposed method and that its performance is comparable to the state-of-the-art system performances.

Fine-grained recognition of plants from images.

Science.gov (United States)

Šulc, Milan; Matas, Jiří

2017-01-01

Fine-grained recognition of plants from images is a challenging computer vision task, due to the diverse appearance and complex structure of plants, high intra-class variability and small inter-class differences. We review the state-of-the-art and discuss plant recognition tasks, from identification of plants from specific plant organs to general plant recognition "in the wild". We propose texture analysis and deep learning methods for different plant recognition tasks. The methods are evaluated and compared them to the state-of-the-art. Texture analysis is only applied to images with unambiguous segmentation (bark and leaf recognition), whereas CNNs are only applied when sufficiently large datasets are available. The results provide an insight in the complexity of different plant recognition tasks. The proposed methods outperform the state-of-the-art in leaf and bark classification and achieve very competitive results in plant recognition "in the wild". The results suggest that recognition of segmented leaves is practically a solved problem, when high volumes of training data are available. The generality and higher capacity of state-of-the-art CNNs makes them suitable for plant recognition "in the wild" where the views on plant organs or plants vary significantly and the difficulty is increased by occlusions and background clutter.

Transfer-Appropriate Processing in Recognition Memory: Perceptual and Conceptual Effects on Recognition Memory Depend on Task Demands

Science.gov (United States)

Parks, Colleen M.

2013-01-01

Research examining the importance of surface-level information to familiarity in recognition memory tasks is mixed: Sometimes it affects recognition and sometimes it does not. One potential explanation of the inconsistent findings comes from the ideas of dual process theory of recognition and the transfer-appropriate processing framework, which…

Recognition

DEFF Research Database (Denmark)

Gimmler, Antje

2017-01-01

In this article, I shall examine the cognitive, heuristic and theoretical functions of the concept of recognition. To evaluate both the explanatory power and the limitations of a sociological concept, the theory construction must be analysed and its actual productivity for sociological theory mus...

Pattern Recognition Methods and Features Selection for Speech Emotion Recognition System.

Science.gov (United States)

Partila, Pavol; Voznak, Miroslav; Tovarek, Jaromir

2015-01-01

The impact of the classification method and features selection for the speech emotion recognition accuracy is discussed in this paper. Selecting the correct parameters in combination with the classifier is an important part of reducing the complexity of system computing. This step is necessary especially for systems that will be deployed in real-time applications. The reason for the development and improvement of speech emotion recognition systems is wide usability in nowadays automatic voice controlled systems. Berlin database of emotional recordings was used in this experiment. Classification accuracy of artificial neural networks, k-nearest neighbours, and Gaussian mixture model is measured considering the selection of prosodic, spectral, and voice quality features. The purpose was to find an optimal combination of methods and group of features for stress detection in human speech. The research contribution lies in the design of the speech emotion recognition system due to its accuracy and efficiency.

Face Recognition in Humans and Machines

Science.gov (United States)

O'Toole, Alice; Tistarelli, Massimo

The study of human face recognition by psychologists and neuroscientists has run parallel to the development of automatic face recognition technologies by computer scientists and engineers. In both cases, there are analogous steps of data acquisition, image processing, and the formation of representations that can support the complex and diverse tasks we accomplish with faces. These processes can be understood and compared in the context of their neural and computational implementations. In this chapter, we present the essential elements of face recognition by humans and machines, taking a perspective that spans psychological, neural, and computational approaches. From the human side, we overview the methods and techniques used in the neurobiology of face recognition, the underlying neural architecture of the system, the role of visual attention, and the nature of the representations that emerges. From the computational side, we discuss face recognition technologies and the strategies they use to overcome challenges to robust operation over viewing parameters. Finally, we conclude the chapter with a look at some recent studies that compare human and machine performances at face recognition.

An Introduction to Face Recognition Technology

Directory of Open Access Journals (Sweden)

Shang-Hung Lin

2000-01-01

Full Text Available Recently face recognition is attracting much attention in the society of network multimedia information access.  Areas such as network security, content indexing and retrieval, and video compression benefits from face recognition technology because "people" are the center of attention in a lot of video.  Network access control via face recognition not only makes hackers virtually impossible to steal one's "password", but also increases the user-friendliness in human-computer interaction.  Indexing and/or retrieving video data based on the appearances of particular persons will be useful for users such as news reporters, political scientists, and moviegoers.  For the applications of videophone and teleconferencing, the assistance of face recognition also provides a more efficient coding scheme.  In this paper, we give an introductory course of this new information processing technology.  The paper shows the readers the generic framework for the face recognition system, and the variants that are frequently encountered by the face recognizer.  Several famous face recognition algorithms, such as eigenfaces and neural networks, will also be explained.

Paradigms in object recognition

International Nuclear Information System (INIS)

Mutihac, R.; Mutihac, R.C.

1999-09-01

A broad range of approaches has been proposed and applied for the complex and rather difficult task of object recognition that involves the determination of object characteristics and object classification into one of many a priori object types. Our paper revises briefly the three main different paradigms in pattern recognition, namely Bayesian statistics, neural networks, and expert systems. (author)

The coevolution of recognition and social behavior.

Science.gov (United States)

Smead, Rory; Forber, Patrick

2016-05-26

Recognition of behavioral types can facilitate the evolution of cooperation by enabling altruistic behavior to be directed at other cooperators and withheld from defectors. While much is known about the tendency for recognition to promote cooperation, relatively little is known about whether such a capacity can coevolve with the social behavior it supports. Here we use evolutionary game theory and multi-population dynamics to model the coevolution of social behavior and recognition. We show that conditional harming behavior enables the evolution and stability of social recognition, whereas conditional helping leads to a deterioration of recognition ability. Expanding the model to include a complex game where both helping and harming interactions are possible, we find that conditional harming behavior can stabilize recognition, and thereby lead to the evolution of conditional helping. Our model identifies a novel hypothesis for the evolution of cooperation: conditional harm may have coevolved with recognition first, thereby helping to establish the mechanisms necessary for the evolution of cooperation.

The recognition heuristic: A decade of research

Directory of Open Access Journals (Sweden)

Gerd Gigerenzer

2011-02-01

Full Text Available The recognition heuristic exploits the basic psychological capacity for recognition in order to make inferences about unknown quantities in the world. In this article, we review and clarify issues that emerged from our initial work (Goldstein and Gigerenzer, 1999, 2002, including the distinction between a recognition and an evaluation process. There is now considerable evidence that (i the recognition heuristic predicts the inferences of a substantial proportion of individuals consistently, even in the presence of one or more contradicting cues, (ii people are adaptive decision makers in that accordance increases with larger recognition validity and decreases in situations when the validity is low or wholly indeterminable, and (iii in the presence of contradicting cues, some individuals appear to select different strategies. Little is known about these individual differences, or how to precisely model the alternative strategies. Although some researchers have attributed judgments inconsistent with the use of the recognition heuristic to compensatory processing, little research on such compensatory models has been reported. We discuss extensions of the recognition model, open questions, unanticipated results, and the surprising predictive power of recognition in forecasting.

Bilingual Language Switching: Production vs. Recognition

Science.gov (United States)

Mosca, Michela; de Bot, Kees

2017-01-01

This study aims at assessing how bilinguals select words in the appropriate language in production and recognition while minimizing interference from the non-appropriate language. Two prominent models are considered which assume that when one language is in use, the other is suppressed. The Inhibitory Control (IC) model suggests that, in both production and recognition, the amount of inhibition on the non-target language is greater for the stronger compared to the weaker language. In contrast, the Bilingual Interactive Activation (BIA) model proposes that, in language recognition, the amount of inhibition on the weaker language is stronger than otherwise. To investigate whether bilingual language production and recognition can be accounted for by a single model of bilingual processing, we tested a group of native speakers of Dutch (L1), advanced speakers of English (L2) in a bilingual recognition and production task. Specifically, language switching costs were measured while participants performed a lexical decision (recognition) and a picture naming (production) task involving language switching. Results suggest that while in language recognition the amount of inhibition applied to the non-appropriate language increases along with its dominance as predicted by the IC model, in production the amount of inhibition applied to the non-relevant language is not related to language dominance, but rather it may be modulated by speakers' unconscious strategies to foster the weaker language. This difference indicates that bilingual language recognition and production might rely on different processing mechanisms and cannot be accounted within one of the existing models of bilingual language processing. PMID:28638361

Stereotype Associations and Emotion Recognition

NARCIS (Netherlands)

Bijlstra, Gijsbert; Holland, Rob W.; Dotsch, Ron; Hugenberg, Kurt; Wigboldus, Daniel H. J.

We investigated whether stereotype associations between specific emotional expressions and social categories underlie stereotypic emotion recognition biases. Across two studies, we replicated previously documented stereotype biases in emotion recognition using both dynamic (Study 1) and static

Galeotti on recognition as inclusion

DEFF Research Database (Denmark)

Lægaard, Sune

2008-01-01

Anna Elisabetta Galeotti's theory of 'toleration as recognition' has been criticised by Peter Jones for being conceptually incoherent, since liberal toleration presupposes a negative attitude to differences, whereas multicultural recognition requires positive affirmation hereof. The paper spells ...

Recognition of names of eminent psychologists.

Science.gov (United States)

Duncan, C P

1976-10-01

Faculty members, graduate students, undergraduate majors, and introductory psychology students checked those names they recognized in the list of 228 deceased psychologists, rated for eminence, provided by Annin, Boring, and Watson. Mean percentage recognition was less than 50% for the 128 American psychologists, and less than 25% for the 100 foreign psychologists, by the faculty subjects. The other three groups of subjects gave even lower recognition scores. Recognition was probably also influenced by recency; median year of death of the American psychologists was 1955, of the foreign psychologists, 1943. High recognition (defined as recognition by 80% or more of the faculty group) was achieved by only 34 psychologists, almost all of them American. These highly recognized psychologists also had high eminence ratings, but there was an equal number of psychologists with high eminence ratings that were poorly recognized.

Electrolarynx Voice Recognition Utilizing Pulse Coupled Neural Network

Directory of Open Access Journals (Sweden)

Fatchul Arifin

2010-08-01

Full Text Available The laryngectomies patient has no ability to speak normally because their vocal chords have been removed. The easiest option for the patient to speak again is by using electrolarynx speech. This tool is placed on the lower chin. Vibration of the neck while speaking is used to produce sound. Meanwhile, the technology of "voice recognition" has been growing very rapidly. It is expected that the technology of "voice recognition" can also be used by laryngectomies patients who use electrolarynx.This paper describes a system for electrolarynx speech recognition. Two main parts of the system are feature extraction and pattern recognition. The Pulse Coupled Neural Network – PCNN is used to extract the feature and characteristic of electrolarynx speech. Varying of β (one of PCNN parameter also was conducted. Multi layer perceptron is used to recognize the sound patterns. There are two kinds of recognition conducted in this paper: speech recognition and speaker recognition. The speech recognition recognizes specific speech from every people. Meanwhile, speaker recognition recognizes specific speech from specific person. The system ran well. The "electrolarynx speech recognition" has been tested by recognizing of “A” and "not A" voice. The results showed that the system had 94.4% validation. Meanwhile, the electrolarynx speaker recognition has been tested by recognizing of “saya” voice from some different speakers. The results showed that the system had 92.2% validation. Meanwhile, the best β parameter of PCNN for electrolarynx recognition is 3.

[Face recognition in patients with schizophrenia].

Science.gov (United States)

Doi, Hirokazu; Shinohara, Kazuyuki

2012-07-01

It is well known that patients with schizophrenia show severe deficiencies in social communication skills. These deficiencies are believed to be partly derived from abnormalities in face recognition. However, the exact nature of these abnormalities exhibited by schizophrenic patients with respect to face recognition has yet to be clarified. In the present paper, we review the main findings on face recognition deficiencies in patients with schizophrenia, particularly focusing on abnormalities in the recognition of facial expression and gaze direction, which are the primary sources of information of others' mental states. The existing studies reveal that the abnormal recognition of facial expression and gaze direction in schizophrenic patients is attributable to impairments in both perceptual processing of visual stimuli, and cognitive-emotional responses to social information. Furthermore, schizophrenic patients show malfunctions in distributed neural regions, ranging from the fusiform gyrus recruited in the structural encoding of facial stimuli, to the amygdala which plays a primary role in the detection of the emotional significance of stimuli. These findings were obtained from research in patient groups with heterogeneous characteristics. Because previous studies have indicated that impairments in face recognition in schizophrenic patients might vary according to the types of symptoms, it is of primary importance to compare the nature of face recognition deficiencies and the impairments of underlying neural functions across sub-groups of patients.

Pattern Recognition Methods and Features Selection for Speech Emotion Recognition System

Directory of Open Access Journals (Sweden)

Pavol Partila

2015-01-01

Full Text Available The impact of the classification method and features selection for the speech emotion recognition accuracy is discussed in this paper. Selecting the correct parameters in combination with the classifier is an important part of reducing the complexity of system computing. This step is necessary especially for systems that will be deployed in real-time applications. The reason for the development and improvement of speech emotion recognition systems is wide usability in nowadays automatic voice controlled systems. Berlin database of emotional recordings was used in this experiment. Classification accuracy of artificial neural networks, k-nearest neighbours, and Gaussian mixture model is measured considering the selection of prosodic, spectral, and voice quality features. The purpose was to find an optimal combination of methods and group of features for stress detection in human speech. The research contribution lies in the design of the speech emotion recognition system due to its accuracy and efficiency.

Facial recognition in education system

Science.gov (United States)

Krithika, L. B.; Venkatesh, K.; Rathore, S.; Kumar, M. Harish

2017-11-01

Human beings exploit emotions comprehensively for conveying messages and their resolution. Emotion detection and face recognition can provide an interface between the individuals and technologies. The most successful applications of recognition analysis are recognition of faces. Many different techniques have been used to recognize the facial expressions and emotion detection handle varying poses. In this paper, we approach an efficient method to recognize the facial expressions to track face points and distances. This can automatically identify observer face movements and face expression in image. This can capture different aspects of emotion and facial expressions.

School IPM Recognition and Certification

Science.gov (United States)

Schools and school districts can get support and recognition for implementation of school IPM. EPA is developing a program to provide recognition for school districts that are working towards or have achieved a level of success with school IPM programs.

Implicit recognition based on lateralized perceptual fluency.

Science.gov (United States)

Vargas, Iliana M; Voss, Joel L; Paller, Ken A

2012-02-06

In some circumstances, accurate recognition of repeated images in an explicit memory test is driven by implicit memory. We propose that this "implicit recognition" results from perceptual fluency that influences responding without awareness of memory retrieval. Here we examined whether recognition would vary if images appeared in the same or different visual hemifield during learning and testing. Kaleidoscope images were briefly presented left or right of fixation during divided-attention encoding. Presentation in the same visual hemifield at test produced higher recognition accuracy than presentation in the opposite visual hemifield, but only for guess responses. These correct guesses likely reflect a contribution from implicit recognition, given that when the stimulated visual hemifield was the same at study and test, recognition accuracy was higher for guess responses than for responses with any level of confidence. The dramatic difference in guessing accuracy as a function of lateralized perceptual overlap between study and test suggests that implicit recognition arises from memory storage in visual cortical networks that mediate repetition-induced fluency increments.

Fusing Facial Features for Face Recognition

Directory of Open Access Journals (Sweden)

Jamal Ahmad Dargham

2012-06-01

Full Text Available Face recognition is an important biometric method because of its potential applications in many fields, such as access control, surveillance, and human-computer interaction. In this paper, a face recognition system that fuses the outputs of three face recognition systems based on Gabor jets is presented. The first system uses the magnitude, the second uses the phase, and the third uses the phase-weighted magnitude of the jets. The jets are generated from facial landmarks selected using three selection methods. It was found out that fusing the facial features gives better recognition rate than either facial feature used individually regardless of the landmark selection method.

Face Recognition and Tracking in Videos

Directory of Open Access Journals (Sweden)

Swapnil Vitthal Tathe

2017-07-01

Full Text Available Advancement in computer vision technology and availability of video capturing devices such as surveillance cameras has evoked new video processing applications. The research in video face recognition is mostly biased towards law enforcement applications. Applications involves human recognition based on face and iris, human computer interaction, behavior analysis, video surveillance etc. This paper presents face tracking framework that is capable of face detection using Haar features, recognition using Gabor feature extraction, matching using correlation score and tracking using Kalman filter. The method has good recognition rate for real-life videos and robust performance to changes due to illumination, environmental factors, scale, pose and orientations.

The Recognition Of Fatigue

DEFF Research Database (Denmark)

Elsass, Peter; Jensen, Bodil; Mørup, Rikke

2007-01-01

Elsass P., Jensen B., Morup R., Thogersen M.H. (2007). The Recognition Of Fatigue: A qualitative study of life-stories from rehabilitation clients. International Journal of Psychosocial Rehabilitation. 11 (2), 75-87......Elsass P., Jensen B., Morup R., Thogersen M.H. (2007). The Recognition Of Fatigue: A qualitative study of life-stories from rehabilitation clients. International Journal of Psychosocial Rehabilitation. 11 (2), 75-87...

Forecasting elections with mere recognition from small, lousy samples: A comparison of collective recognition, wisdom of crowds, and representative polls

Directory of Open Access Journals (Sweden)

Wolfgang Gaissmeier

2011-02-01

Full Text Available We investigated the extent to which the human capacity for recognition helps to forecast political elections: We compared naive recognition-based election forecasts computed from convenience samples of citizens' recognition of party names to (i standard polling forecasts computed from representative samples of citizens' voting intentions, and to (ii simple---and typically very accurate---wisdom-of-crowds-forecasts computed from the same convenience samples of citizens' aggregated hunches about election results. Results from four major German elections show that mere recognition of party names forecast the parties' electoral success fairly well. Recognition-based forecasts were most competitive with the other models when forecasting the smaller parties' success and for small sample sizes. However, wisdom-of-crowds-forecasts outperformed recognition-based forecasts in most cases. It seems that wisdom-of-crowds-forecasts are able to draw on the benefits of recognition while at the same time avoiding its downsides, such as lack of discrimination among very famous parties or recognition caused by factors unrelated to electoral success. Yet it seems that a simple extension of the recognition-based forecasts---asking people what proportion of the population would recognize a party instead of whether they themselves recognize it---is also able to eliminate these downsides.

Indoor navigation by image recognition

Science.gov (United States)

Choi, Io Teng; Leong, Chi Chong; Hong, Ka Wo; Pun, Chi-Man

2017-07-01

With the progress of smartphones hardware, it is simple on smartphone using image recognition technique such as face detection. In addition, indoor navigation system development is much slower than outdoor navigation system. Hence, this research proves a usage of image recognition technique for navigation in indoor environment. In this paper, we introduced an indoor navigation application that uses the indoor environment features to locate user's location and a route calculating algorithm to generate an appropriate path for user. The application is implemented on Android smartphone rather than iPhone. Yet, the application design can also be applied on iOS because the design is implemented without using special features only for Android. We found that digital navigation system provides better and clearer location information than paper map. Also, the indoor environment is ideal for Image recognition processing. Hence, the results motivate us to design an indoor navigation system using image recognition.

Episodic Short-Term Recognition Requires Encoding into Visual Working Memory: Evidence from Probe Recognition after Letter Report.

Science.gov (United States)

Poth, Christian H; Schneider, Werner X

2016-01-01

Human vision is organized in discrete processing episodes (e.g., eye fixations or task-steps). Object information must be transmitted across episodes to enable episodic short-term recognition: recognizing whether a current object has been seen in a previous episode. We ask whether episodic short-term recognition presupposes that objects have been encoded into capacity-limited visual working memory (VWM), which retains visual information for report. Alternatively, it could rely on the activation of visual features or categories that occurs before encoding into VWM. We assessed the dependence of episodic short-term recognition on VWM by a new paradigm combining letter report and probe recognition. Participants viewed displays of 10 letters and reported as many as possible after a retention interval (whole report). Next, participants viewed a probe letter and indicated whether it had been one of the 10 letters (probe recognition). In Experiment 1, probe recognition was more accurate for letters that had been encoded into VWM (reported letters) compared with non-encoded letters (non-reported letters). Interestingly, those letters that participants reported in their whole report had been near to one another within the letter displays. This suggests that the encoding into VWM proceeded in a spatially clustered manner. In Experiment 2, participants reported only one of 10 letters (partial report) and probes either referred to this letter, to letters that had been near to it, or far from it. Probe recognition was more accurate for near than for far letters, although none of these letters had to be reported. These findings indicate that episodic short-term recognition is constrained to a small number of simultaneously presented objects that have been encoded into VWM.

Episodic Short-Term Recognition Requires Encoding into Visual Working Memory: Evidence from Probe Recognition after Letter Report

Directory of Open Access Journals (Sweden)

Christian H. Poth

2016-09-01

Full Text Available Human vision is organized in discrete processing episodes (e.g. eye fixations or task-steps. Object information must be transmitted across episodes to enable episodic short-term recognition: recognizing whether a current object has been seen in a previous episode. We ask whether episodic short-term recognition presupposes that objects have been encoded into capacity-limited visual working memory (VWM, which retains visual information for report. Alternatively, it could rely on the activation of visual features or categories that occurs before encoding into VWM. We assessed the dependence of episodic short-term recognition on VWM by a new paradigm combining letter report and probe recognition. Participants viewed displays of ten letters and reported as many as possible after a retention interval (whole report. Next, participants viewed a probe letter and indicated whether it had been one of the ten letters (probe recognition. In Experiment 1, probe recognition was more accurate for letters that had been encoded into VWM (reported letters compared with non-encoded letters (non-reported letters. Interestingly, those letters that participants reported in their whole report had been near to one another within the letter displays. This suggests that the encoding into VWM proceeded in a spatially clustered manner. In Experiment 2 participants reported only one of ten letters (partial report and probes either referred to this letter, to letters that had been near to it, or far from it. Probe recognition was more accurate for near than for far letters, although none of these letters had to be reported. These findings indicate that episodic short-term recognition is constrained to a small number of simultaneously presented objects that have been encoded into VWM.

Computational intelligence in multi-feature visual pattern recognition hand posture and face recognition using biologically inspired approaches

CERN Document Server

Pisharady, Pramod Kumar; Poh, Loh Ai

2014-01-01

This book presents a collection of computational intelligence algorithms that addresses issues in visual pattern recognition such as high computational complexity, abundance of pattern features, sensitivity to size and shape variations and poor performance against complex backgrounds. The book has 3 parts. Part 1 describes various research issues in the field with a survey of the related literature. Part 2 presents computational intelligence based algorithms for feature selection and classification. The algorithms are discriminative and fast. The main application area considered is hand posture recognition. The book also discusses utility of these algorithms in other visual as well as non-visual pattern recognition tasks including face recognition, general object recognition and cancer / tumor classification. Part 3 presents biologically inspired algorithms for feature extraction. The visual cortex model based features discussed have invariance with respect to appearance and size of the hand, and provide good...

Mirror self-recognition: a review and critique of attempts to promote and engineer self-recognition in primates.

Science.gov (United States)

Anderson, James R; Gallup, Gordon G

2015-10-01

We review research on reactions to mirrors and self-recognition in nonhuman primates, focusing on methodological issues. Starting with the initial demonstration in chimpanzees in 1970 and subsequent attempts to extend this to other species, self-recognition in great apes is discussed with emphasis on spontaneous manifestations of mirror-guided self-exploration as well as spontaneous use of the mirror to investigate foreign marks on otherwise nonvisible body parts-the mark test. Attempts to show self-recognition in other primates are examined with particular reference to the lack of convincing examples of spontaneous mirror-guided self-exploration, and efforts to engineer positive mark test responses by modifying the test or using conditioning techniques. Despite intensive efforts to demonstrate self-recognition in other primates, we conclude that to date there is no compelling evidence that prosimians, monkeys, or lesser apes-gibbons and siamangs-are capable of mirror self-recognition.

FILTWAM and Voice Emotion Recognition

NARCIS (Netherlands)

Bahreini, Kiavash; Nadolski, Rob; Westera, Wim

2014-01-01

This paper introduces the voice emotion recognition part of our framework for improving learning through webcams and microphones (FILTWAM). This framework enables multimodal emotion recognition of learners during game-based learning. The main goal of this study is to validate the use of microphone

Random-Profiles-Based 3D Face Recognition System

Directory of Open Access Journals (Sweden)

Joongrock Kim

2014-03-01

Full Text Available In this paper, a noble nonintrusive three-dimensional (3D face modeling system for random-profile-based 3D face recognition is presented. Although recent two-dimensional (2D face recognition systems can achieve a reliable recognition rate under certain conditions, their performance is limited by internal and external changes, such as illumination and pose variation. To address these issues, 3D face recognition, which uses 3D face data, has recently received much attention. However, the performance of 3D face recognition highly depends on the precision of acquired 3D face data, while also requiring more computational power and storage capacity than 2D face recognition systems. In this paper, we present a developed nonintrusive 3D face modeling system composed of a stereo vision system and an invisible near-infrared line laser, which can be directly applied to profile-based 3D face recognition. We further propose a novel random-profile-based 3D face recognition method that is memory-efficient and pose-invariant. The experimental results demonstrate that the reconstructed 3D face data consists of more than 50 k 3D point clouds and a reliable recognition rate against pose variation.

Flexible Piezoelectric Sensor-Based Gait Recognition

Directory of Open Access Journals (Sweden)

Youngsu Cha

2018-02-01

Full Text Available Most motion recognition research has required tight-fitting suits for precise sensing. However, tight-suit systems have difficulty adapting to real applications, because people normally wear loose clothes. In this paper, we propose a gait recognition system with flexible piezoelectric sensors in loose clothing. The gait recognition system does not directly sense lower-body angles. It does, however, detect the transition between standing and walking. Specifically, we use the signals from the flexible sensors attached to the knee and hip parts on loose pants. We detect the periodic motion component using the discrete time Fourier series from the signal during walking. We adapt the gait detection method to a real-time patient motion and posture monitoring system. In the monitoring system, the gait recognition operates well. Finally, we test the gait recognition system with 10 subjects, for which the proposed system successfully detects walking with a success rate over 93 %.

Iris recognition via plenoptic imaging

Science.gov (United States)

Santos-Villalobos, Hector J.; Boehnen, Chris Bensing; Bolme, David S.

2017-11-07

Iris recognition can be accomplished for a wide variety of eye images by using plenoptic imaging. Using plenoptic technology, it is possible to correct focus after image acquisition. One example technology reconstructs images having different focus depths and stitches them together, resulting in a fully focused image, even in an off-angle gaze scenario. Another example technology determines three-dimensional data for an eye and incorporates it into an eye model used for iris recognition processing. Another example technology detects contact lenses. Application of the technologies can result in improved iris recognition under a wide variety of scenarios.

Face Recognition using Approximate Arithmetic

DEFF Research Database (Denmark)

Marso, Karol

Face recognition is image processing technique which aims to identify human faces and found its use in various different fields for example in security. Throughout the years this field evolved and there are many approaches and many different algorithms which aim to make the face recognition as effective...... processing applications the results do not need to be completely precise and use of the approximate arithmetic can lead to reduction in terms of delay, space and power consumption. In this paper we examine possible use of approximate arithmetic in face recognition using Eigenfaces algorithm....

Pedestrian recognition using automotive radar sensors

OpenAIRE

A. Bartsch; F. Fitzek; R. H. Rasshofer

2012-01-01

The application of modern series production automotive radar sensors to pedestrian recognition is an important topic in research on future driver assistance systems. The aim of this paper is to understand the potential and limits of such sensors in pedestrian recognition. This knowledge could be used to develop next generation radar sensors with improved pedestrian recognition capabilities. A new raw radar data signal processing algorithm is proposed that allows deep insight...

Cognitive object recognition system (CORS)

Science.gov (United States)

Raju, Chaitanya; Varadarajan, Karthik Mahesh; Krishnamurthi, Niyant; Xu, Shuli; Biederman, Irving; Kelley, Troy

2010-04-01

We have developed a framework, Cognitive Object Recognition System (CORS), inspired by current neurocomputational models and psychophysical research in which multiple recognition algorithms (shape based geometric primitives, 'geons,' and non-geometric feature-based algorithms) are integrated to provide a comprehensive solution to object recognition and landmarking. Objects are defined as a combination of geons, corresponding to their simple parts, and the relations among the parts. However, those objects that are not easily decomposable into geons, such as bushes and trees, are recognized by CORS using "feature-based" algorithms. The unique interaction between these algorithms is a novel approach that combines the effectiveness of both algorithms and takes us closer to a generalized approach to object recognition. CORS allows recognition of objects through a larger range of poses using geometric primitives and performs well under heavy occlusion - about 35% of object surface is sufficient. Furthermore, geon composition of an object allows image understanding and reasoning even with novel objects. With reliable landmarking capability, the system improves vision-based robot navigation in GPS-denied environments. Feasibility of the CORS system was demonstrated with real stereo images captured from a Pioneer robot. The system can currently identify doors, door handles, staircases, trashcans and other relevant landmarks in the indoor environment.

An audiovisual emotion recognition system

Science.gov (United States)

Han, Yi; Wang, Guoyin; Yang, Yong; He, Kun

2007-12-01

Human emotions could be expressed by many bio-symbols. Speech and facial expression are two of them. They are both regarded as emotional information which is playing an important role in human-computer interaction. Based on our previous studies on emotion recognition, an audiovisual emotion recognition system is developed and represented in this paper. The system is designed for real-time practice, and is guaranteed by some integrated modules. These modules include speech enhancement for eliminating noises, rapid face detection for locating face from background image, example based shape learning for facial feature alignment, and optical flow based tracking algorithm for facial feature tracking. It is known that irrelevant features and high dimensionality of the data can hurt the performance of classifier. Rough set-based feature selection is a good method for dimension reduction. So 13 speech features out of 37 ones and 10 facial features out of 33 ones are selected to represent emotional information, and 52 audiovisual features are selected due to the synchronization when speech and video fused together. The experiment results have demonstrated that this system performs well in real-time practice and has high recognition rate. Our results also show that the work in multimodules fused recognition will become the trend of emotion recognition in the future.

Face recognition in the thermal infrared domain

Science.gov (United States)

Kowalski, M.; Grudzień, A.; Palka, N.; Szustakowski, M.

2017-10-01

Biometrics refers to unique human characteristics. Each unique characteristic may be used to label and describe individuals and for automatic recognition of a person based on physiological or behavioural properties. One of the most natural and the most popular biometric trait is a face. The most common research methods on face recognition are based on visible light. State-of-the-art face recognition systems operating in the visible light spectrum achieve very high level of recognition accuracy under controlled environmental conditions. Thermal infrared imagery seems to be a promising alternative or complement to visible range imaging due to its relatively high resistance to illumination changes. A thermal infrared image of the human face presents its unique heat-signature and can be used for recognition. The characteristics of thermal images maintain advantages over visible light images, and can be used to improve algorithms of human face recognition in several aspects. Mid-wavelength or far-wavelength infrared also referred to as thermal infrared seems to be promising alternatives. We present the study on 1:1 recognition in thermal infrared domain. The two approaches we are considering are stand-off face verification of non-moving person as well as stop-less face verification on-the-move. The paper presents methodology of our studies and challenges for face recognition systems in the thermal infrared domain.

Micro-Recognition - Erving Goffman as Recognition Thinker

DEFF Research Database (Denmark)

Jacobsen, Michael Hviid; Kristiansen, Søren

2009-01-01

and civil inattention guide the conduct of people in many of their face-to-face encounters with each other. This article therefore shows how Goffman may in fact supplement many of the most fashionable and celebrated contemporary recognition theories as advanced by e.g. Nancy Fraser, Charles Taylor or Axel...

Data structures, computer graphics, and pattern recognition

CERN Document Server

Klinger, A; Kunii, T L

1977-01-01

Data Structures, Computer Graphics, and Pattern Recognition focuses on the computer graphics and pattern recognition applications of data structures methodology.This book presents design related principles and research aspects of the computer graphics, system design, data management, and pattern recognition tasks. The topics include the data structure design, concise structuring of geometric data for computer aided design, and data structures for pattern recognition algorithms. The survey of data structures for computer graphics systems, application of relational data structures in computer gr

Auditory Modeling for Noisy Speech Recognition

National Research Council Canada - National Science Library

2000-01-01

... digital filtering for noise cancellation which interfaces to speech recognition software. It uses auditory features in speech recognition training, and provides applications to multilingual spoken language translation...

Forensic Face Recognition: A Survey

NARCIS (Netherlands)

Ali, Tauseef; Veldhuis, Raymond N.J.; Spreeuwers, Lieuwe Jan

2010-01-01

Beside a few papers which focus on the forensic aspects of automatic face recognition, there is not much published about it in contrast to the literature on developing new techniques and methodologies for biometric face recognition. In this report, we review forensic facial identification which is

The recognition heuristic: a review of theory and tests.

Science.gov (United States)

Pachur, Thorsten; Todd, Peter M; Gigerenzer, Gerd; Schooler, Lael J; Goldstein, Daniel G

2011-01-01

The recognition heuristic is a prime example of how, by exploiting a match between mind and environment, a simple mental strategy can lead to efficient decision making. The proposal of the heuristic initiated a debate about the processes underlying the use of recognition in decision making. We review research addressing four key aspects of the recognition heuristic: (a) that recognition is often an ecologically valid cue; (b) that people often follow recognition when making inferences; (c) that recognition supersedes further cue knowledge; (d) that its use can produce the less-is-more effect - the phenomenon that lesser states of recognition knowledge can lead to more accurate inferences than more complete states. After we contrast the recognition heuristic to other related concepts, including availability and fluency, we carve out, from the existing findings, some boundary conditions of the use of the recognition heuristic as well as key questions for future research. Moreover, we summarize developments concerning the connection of the recognition heuristic with memory models. We suggest that the recognition heuristic is used adaptively and that, compared to other cues, recognition seems to have a special status in decision making. Finally, we discuss how systematic ignorance is exploited in other cognitive mechanisms (e.g., estimation and preference).

The Recognition Heuristic: A Review of Theory and Tests

Directory of Open Access Journals (Sweden)

Thorsten ePachur

2011-07-01

Full Text Available The recognition heuristic is a prime example of how, by exploiting a match between mind and environment, a simple mental strategy can lead to efficient decision making. The proposal of the heuristic initiated a debate about the processes underlying the use of recognition in decision making. We review research addressing four key aspects of the recognition heuristic: (a that recognition is often an ecologically valid cue; (b that people often follow recognition when making inferences; (c that recognition supersedes further cue knowledge; (d that its use can produce the less-is-more effect—the phenomenon that lesser states of recognition knowledge can lead to more accurate inferences than more complete states. After we contrast the recognition heuristic to other related concepts, including availability and fluency, we carve out, from the existing findings, some boundary conditions of the use of the recognition heuristic as well as key questions for future research. Moreover, we summarize developments concerning the connection of the recognition heuristic with memory models. We suggest that the recognition heuristic is used adaptively and that, compared to other cues, recognition seems to have a special status in decision making. Finally, we discuss how systematic ignorance is exploited in other cognitive mechanisms (e.g., estimation and preference.

The Recognition Heuristic: A Review of Theory and Tests

Science.gov (United States)

Pachur, Thorsten; Todd, Peter M.; Gigerenzer, Gerd; Schooler, Lael J.; Goldstein, Daniel G.

2011-01-01

The recognition heuristic is a prime example of how, by exploiting a match between mind and environment, a simple mental strategy can lead to efficient decision making. The proposal of the heuristic initiated a debate about the processes underlying the use of recognition in decision making. We review research addressing four key aspects of the recognition heuristic: (a) that recognition is often an ecologically valid cue; (b) that people often follow recognition when making inferences; (c) that recognition supersedes further cue knowledge; (d) that its use can produce the less-is-more effect – the phenomenon that lesser states of recognition knowledge can lead to more accurate inferences than more complete states. After we contrast the recognition heuristic to other related concepts, including availability and fluency, we carve out, from the existing findings, some boundary conditions of the use of the recognition heuristic as well as key questions for future research. Moreover, we summarize developments concerning the connection of the recognition heuristic with memory models. We suggest that the recognition heuristic is used adaptively and that, compared to other cues, recognition seems to have a special status in decision making. Finally, we discuss how systematic ignorance is exploited in other cognitive mechanisms (e.g., estimation and preference). PMID:21779266

Research on Face Recognition Based on Embedded System

Directory of Open Access Journals (Sweden)

Hong Zhao

2013-01-01

Full Text Available Because a number of image feature data to store, complex calculation to execute during the face recognition, therefore the face recognition process was realized only by PCs with high performance. In this paper, the OpenCV facial Haar-like features were used to identify face region; the Principal Component Analysis (PCA was employed in quick extraction of face features and the Euclidean Distance was also adopted in face recognition; as thus, data amount and computational complexity would be reduced effectively in face recognition, and the face recognition could be carried out on embedded platform. Finally, based on Tiny6410 embedded platform, a set of embedded face recognition systems was constructed. The test results showed that the system has stable operation and high recognition rate can be used in portable and mobile identification and authentication.

L2 Word Recognition: Influence of L1 Orthography on Multi-Syllabic Word Recognition

Science.gov (United States)

Hamada, Megumi

2017-01-01

L2 reading research suggests that L1 orthographic experience influences L2 word recognition. Nevertheless, the findings on multi-syllabic words in English are still limited despite the fact that a vast majority of words are multi-syllabic. The study investigated whether L1 orthography influences the recognition of multi-syllabic words, focusing on…

Recognition of Prior Learning, Self-Realisation and Identity within Axel Honneth's Theory of Recognition

Science.gov (United States)

Sandberg, Fredrik; Kubiak, Chris

2013-01-01

This paper argues for the significance of Axel Honneth's theory of recognition for understanding recognition of prior learning (RPL). Case studies of the experiences of RPL by paraprofessional workers in health and social care in the UK and Sweden are used to explicate this significance. The results maintain that there are varying conditions of…

Hidden neural networks: application to speech recognition

DEFF Research Database (Denmark)

Riis, Søren Kamaric

1998-01-01

We evaluate the hidden neural network HMM/NN hybrid on two speech recognition benchmark tasks; (1) task independent isolated word recognition on the Phonebook database, and (2) recognition of broad phoneme classes in continuous speech from the TIMIT database. It is shown how hidden neural networks...

Pattern recognition and string matching

CERN Document Server

Cheng, Xiuzhen

2002-01-01

The research and development of pattern recognition have proven to be of importance in science, technology, and human activity. Many useful concepts and tools from different disciplines have been employed in pattern recognition. Among them is string matching, which receives much theoretical and practical attention. String matching is also an important topic in combinatorial optimization. This book is devoted to recent advances in pattern recognition and string matching. It consists of twenty eight chapters written by different authors, addressing a broad range of topics such as those from classifica­ tion, matching, mining, feature selection, and applications. Each chapter is self-contained, and presents either novel methodological approaches or applications of existing theories and techniques. The aim, intent, and motivation for publishing this book is to pro­ vide a reference tool for the increasing number of readers who depend upon pattern recognition or string matching in some way. This includes student...

Mobile Visual Recognition on Smartphones

Directory of Open Access Journals (Sweden)

Zhenwen Gui

2013-01-01

Full Text Available This paper addresses the recognition of large-scale outdoor scenes on smartphones by fusing outputs of inertial sensors and computer vision techniques. The main contributions can be summarized as follows. Firstly, we propose an ORD (overlap region divide method to plot image position area, which is fast enough to find the nearest visiting area and can also reduce the search range compared with the traditional approaches. Secondly, the vocabulary tree-based approach is improved by introducing GAGCC (gravity-aligned geometric consistency constraint. Our method involves no operation in the high-dimensional feature space and does not assume a global transform between a pair of images. Thus, it substantially reduces the computational complexity and memory usage, which makes the city scale image recognition feasible on the smartphone. Experiments on a collected database including 0.16 million images show that the proposed method demonstrates excellent recognition performance, while maintaining the average recognition time about 1 s.

Recognition Using Classification and Segmentation Scoring

National Research Council Canada - National Science Library

Kimball, Owen; Ostendorf, Mari; Rohlicek, Robin

1992-01-01

.... We describe an approach to connected word recognition that allows the use of segmental information through an explicit decomposition of the recognition criterion into classification and segmentation scoring...

[Measuring impairment of facial affects recognition in schizophrenia. Preliminary study of the facial emotions recognition task (TREF)].

Science.gov (United States)

Gaudelus, B; Virgile, J; Peyroux, E; Leleu, A; Baudouin, J-Y; Franck, N

2015-06-01

The impairment of social cognition, including facial affects recognition, is a well-established trait in schizophrenia, and specific cognitive remediation programs focusing on facial affects recognition have been developed by different teams worldwide. However, even though social cognitive impairments have been confirmed, previous studies have also shown heterogeneity of the results between different subjects. Therefore, assessment of personal abilities should be measured individually before proposing such programs. Most research teams apply tasks based on facial affects recognition by Ekman et al. or Gur et al. However, these tasks are not easily applicable in a clinical exercise. Here, we present the Facial Emotions Recognition Test (TREF), which is designed to identify facial affects recognition impairments in a clinical practice. The test is composed of 54 photos and evaluates abilities in the recognition of six universal emotions (joy, anger, sadness, fear, disgust and contempt). Each of these emotions is represented with colored photos of 4 different models (two men and two women) at nine intensity levels from 20 to 100%. Each photo is presented during 10 seconds; no time limit for responding is applied. The present study compared the scores of the TREF test in a sample of healthy controls (64 subjects) and people with stabilized schizophrenia (45 subjects) according to the DSM IV-TR criteria. We analysed global scores for all emotions, as well as sub scores for each emotion between these two groups, taking into account gender differences. Our results were coherent with previous findings. Applying TREF, we confirmed an impairment in facial affects recognition in schizophrenia by showing significant differences between the two groups in their global results (76.45% for healthy controls versus 61.28% for people with schizophrenia), as well as in sub scores for each emotion except for joy. Scores for women were significantly higher than for men in the population

Object feature extraction and recognition model

International Nuclear Information System (INIS)

Wan Min; Xiang Rujian; Wan Yongxing

2001-01-01

The characteristics of objects, especially flying objects, are analyzed, which include characteristics of spectrum, image and motion. Feature extraction is also achieved. To improve the speed of object recognition, a feature database is used to simplify the data in the source database. The feature vs. object relationship maps are stored in the feature database. An object recognition model based on the feature database is presented, and the way to achieve object recognition is also explained

A Bayesian classifier for symbol recognition

OpenAIRE

Barrat , Sabine; Tabbone , Salvatore; Nourrissier , Patrick

2007-01-01

URL : http://www.buyans.com/POL/UploadedFile/134_9977.pdf; International audience; We present in this paper an original adaptation of Bayesian networks to symbol recognition problem. More precisely, a descriptor combination method, which enables to improve significantly the recognition rate compared to the recognition rates obtained by each descriptor, is presented. In this perspective, we use a simple Bayesian classifier, called naive Bayes. In fact, probabilistic graphical models, more spec...

Hierarchical Recognition Scheme for Human Facial Expression Recognition Systems

Directory of Open Access Journals (Sweden)

Muhammad Hameed Siddiqi

2013-12-01

Full Text Available Over the last decade, human facial expressions recognition (FER has emerged as an important research area. Several factors make FER a challenging research problem. These include varying light conditions in training and test images; need for automatic and accurate face detection before feature extraction; and high similarity among different expressions that makes it difficult to distinguish these expressions with a high accuracy. This work implements a hierarchical linear discriminant analysis-based facial expressions recognition (HL-FER system to tackle these problems. Unlike the previous systems, the HL-FER uses a pre-processing step to eliminate light effects, incorporates a new automatic face detection scheme, employs methods to extract both global and local features, and utilizes a HL-FER to overcome the problem of high similarity among different expressions. Unlike most of the previous works that were evaluated using a single dataset, the performance of the HL-FER is assessed using three publicly available datasets under three different experimental settings: n-fold cross validation based on subjects for each dataset separately; n-fold cross validation rule based on datasets; and, finally, a last set of experiments to assess the effectiveness of each module of the HL-FER separately. Weighted average recognition accuracy of 98.7% across three different datasets, using three classifiers, indicates the success of employing the HL-FER for human FER.

Hierarchical Recognition Scheme for Human Facial Expression Recognition Systems

Science.gov (United States)

Siddiqi, Muhammad Hameed; Lee, Sungyoung; Lee, Young-Koo; Khan, Adil Mehmood; Truc, Phan Tran Ho

2013-01-01

Over the last decade, human facial expressions recognition (FER) has emerged as an important research area. Several factors make FER a challenging research problem. These include varying light conditions in training and test images; need for automatic and accurate face detection before feature extraction; and high similarity among different expressions that makes it difficult to distinguish these expressions with a high accuracy. This work implements a hierarchical linear discriminant analysis-based facial expressions recognition (HL-FER) system to tackle these problems. Unlike the previous systems, the HL-FER uses a pre-processing step to eliminate light effects, incorporates a new automatic face detection scheme, employs methods to extract both global and local features, and utilizes a HL-FER to overcome the problem of high similarity among different expressions. Unlike most of the previous works that were evaluated using a single dataset, the performance of the HL-FER is assessed using three publicly available datasets under three different experimental settings: n-fold cross validation based on subjects for each dataset separately; n-fold cross validation rule based on datasets; and, finally, a last set of experiments to assess the effectiveness of each module of the HL-FER separately. Weighted average recognition accuracy of 98.7% across three different datasets, using three classifiers, indicates the success of employing the HL-FER for human FER. PMID:24316568

Improved RGB-D-T based Face Recognition

DEFF Research Database (Denmark)

Oliu Simon, Marc; Corneanu, Ciprian; Nasrollahi, Kamal

2016-01-01

years. At the same time a multimodal facial recognition is a promising approach. This paper combines the latest successes in both directions by applying deep learning Convolutional Neural Networks (CNN) to the multimodal RGB-D-T based facial recognition problem outperforming previously published results......Reliable facial recognition systems are of crucial importance in various applications from entertainment to security. Thanks to the deep-learning concepts introduced in the field, a significant improvement in the performance of the unimodal facial recognition systems has been observed in the recent...

Unequal recognition, misrecognition and injustice

DEFF Research Database (Denmark)

Lægaard, Sune

2012-01-01

by the state of religious minorities. It argues that state–religion relations can be analysed as relations of recognition, which are not only unequal but also multi-dimensional, and that it is difficult to answer the question whether multi-dimensional recognitive inequalities are unjust or wrong if one...

Degraded character recognition based on gradient pattern

Science.gov (United States)

Babu, D. R. Ramesh; Ravishankar, M.; Kumar, Manish; Wadera, Kevin; Raj, Aakash

2010-02-01

Degraded character recognition is a challenging problem in the field of Optical Character Recognition (OCR). The performance of an optical character recognition depends upon printed quality of the input documents. Many OCRs have been designed which correctly identifies the fine printed documents. But, very few reported work has been found on the recognition of the degraded documents. The efficiency of the OCRs system decreases if the input image is degraded. In this paper, a novel approach based on gradient pattern for recognizing degraded printed character is proposed. The approach makes use of gradient pattern of an individual character for recognition. Experiments were conducted on character image that is either digitally written or a degraded character extracted from historical documents and the results are found to be satisfactory.

[Face recognition in patients with autism spectrum disorders].

Science.gov (United States)

Kita, Yosuke; Inagaki, Masumi

2012-07-01

The present study aimed to review previous research conducted on face recognition in patients with autism spectrum disorders (ASD). Face recognition is a key question in the ASD research field because it can provide clues for elucidating the neural substrates responsible for the social impairment of these patients. Historically, behavioral studies have reported low performance and/or unique strategies of face recognition among ASD patients. However, the performance and strategy of ASD patients is comparable to those of the control group, depending on the experimental situation or developmental stage, suggesting that face recognition of ASD patients is not entirely impaired. Recent brain function studies, including event-related potential and functional magnetic resonance imaging studies, have investigated the cognitive process of face recognition in ASD patients, and revealed impaired function in the brain's neural network comprising the fusiform gyrus and amygdala. This impaired function is potentially involved in the diminished preference for faces, and in the atypical development of face recognition, eliciting symptoms of unstable behavioral characteristics in these patients. Additionally, face recognition in ASD patients is examined from a different perspective, namely self-face recognition, and facial emotion recognition. While the former topic is intimately linked to basic social abilities such as self-other discrimination, the latter is closely associated with mentalizing. Further research on face recognition in ASD patients should investigate the connection between behavioral and neurological specifics in these patients, by considering developmental changes and the spectrum clinical condition of ASD.

Progesterone impairs social recognition in male rats.

Science.gov (United States)

Bychowski, Meaghan E; Auger, Catherine J

2012-04-01

The influence of progesterone in the brain and on the behavior of females is fairly well understood. However, less is known about the effect of progesterone in the male system. In male rats, receptors for progesterone are present in virtually all vasopressin (AVP) immunoreactive cells in the bed nucleus of the stria terminalis (BST) and the medial amygdala (MeA). This colocalization functions to regulate AVP expression, as progesterone and/or progestin receptors (PR)s suppress AVP expression in these same extrahypothalamic regions in the brain. These data suggest that progesterone may influence AVP-dependent behavior. While AVP is implicated in numerous behavioral and physiological functions in rodents, AVP appears essential for social recognition of conspecifics. Therefore, we examined the effects of progesterone on social recognition. We report that progesterone plays an important role in modulating social recognition in the male brain, as progesterone treatment leads to a significant impairment of social recognition in male rats. Moreover, progesterone appears to act on PRs to impair social recognition, as progesterone impairment of social recognition is blocked by a PR antagonist, RU-486. Social recognition is also impaired by a specific progestin agonist, R5020. Interestingly, we show that progesterone does not interfere with either general memory or olfactory processes, suggesting that progesterone seems critically important to social recognition memory. These data provide strong evidence that physiological levels of progesterone can have an important impact on social behavior in male rats. Copyright © 2012 Elsevier Inc. All rights reserved.

RGB-D-T based Face Recognition

DEFF Research Database (Denmark)

Nikisins, Olegs; Nasrollahi, Kamal; Greitans, Modris

2014-01-01

Facial images are of critical importance in many real-world applications from gaming to surveillance. The current literature on facial image analysis, from face detection to face and facial expression recognition, are mainly performed in either RGB, Depth (D), or both of these modalities. But......, such analyzes have rarely included Thermal (T) modality. This paper paves the way for performing such facial analyzes using synchronized RGB-D-T facial images by introducing a database of 51 persons including facial images of different rotations, illuminations, and expressions. Furthermore, a face recognition...... algorithm has been developed to use these images. The experimental results show that face recognition using such three modalities provides better results compared to face recognition in any of such modalities in most of the cases....

Intact suppression of increased false recognition in schizophrenia.

Science.gov (United States)

Weiss, Anthony P; Dodson, Chad S; Goff, Donald C; Schacter, Daniel L; Heckers, Stephan

2002-09-01

Recognition memory is impaired in patients with schizophrenia, as they rely largely on item familiarity, rather than conscious recollection, to make mnemonic decisions. False recognition of novel items (foils) is increased in schizophrenia and may relate to this deficit in conscious recollection. By studying pictures of the target word during encoding, healthy adults can suppress false recognition. This study examined the effect of pictorial encoding on subsequent recognition of repeated foils in patients with schizophrenia. The study included 40 patients with schizophrenia and 32 healthy comparison subjects. After incidental encoding of 60 words or pictures, subjects were tested for recognition of target items intermixed with 60 new foils. These new foils were subsequently repeated following either a two- or 24-word delay. Subjects were instructed to label these repeated foils as new and not to mistake them for old target words. Schizophrenic patients showed greater overall false recognition of repeated foils. The rate of false recognition of repeated foils was lower after picture encoding than after word encoding. Despite higher levels of false recognition of repeated new items, patients and comparison subjects demonstrated a similar degree of false recognition suppression after picture, as compared to word, encoding. Patients with schizophrenia displayed greater false recognition of repeated foils than comparison subjects, suggesting both a decrement of item- (or source-) specific recollection and a consequent reliance on familiarity in schizophrenia. Despite these deficits, presenting pictorial information at encoding allowed schizophrenic subjects to suppress false recognition to a similar degree as the comparison group, implying the intact use of a high-level cognitive strategy in this population.

Prevalence of face recognition deficits in middle childhood.

Science.gov (United States)

Bennetts, Rachel J; Murray, Ebony; Boyce, Tian; Bate, Sarah

2017-02-01

Approximately 2-2.5% of the adult population is believed to show severe difficulties with face recognition, in the absence of any neurological injury-a condition known as developmental prosopagnosia (DP). However, to date no research has attempted to estimate the prevalence of face recognition deficits in children, possibly because there are very few child-friendly, well-validated tests of face recognition. In the current study, we examined face and object recognition in a group of primary school children (aged 5-11 years), to establish whether our tests were suitable for children and to provide an estimate of face recognition difficulties in children. In Experiment 1 (n = 184), children completed a pre-existing test of child face memory, the Cambridge Face Memory Test-Kids (CFMT-K), and a bicycle test with the same format. In Experiment 2 (n = 413), children completed three-alternative forced-choice matching tasks with faces and bicycles. All tests showed good psychometric properties. The face and bicycle tests were well matched for difficulty and showed a similar developmental trajectory. Neither the memory nor the matching tests were suitable to detect impairments in the youngest groups of children, but both tests appear suitable to screen for face recognition problems in middle childhood. In the current sample, 1.2-5.2% of children showed difficulties with face recognition; 1.2-4% showed face-specific difficulties-that is, poor face recognition with typical object recognition abilities. This is somewhat higher than previous adult estimates: It is possible that face matching tests overestimate the prevalence of face recognition difficulties in children; alternatively, some children may "outgrow" face recognition difficulties.

Self-Recognition in Autistic Children.

Science.gov (United States)

Dawson, Geraldine; McKissick, Fawn Celeste

1984-01-01

Fifteen autistic children (four to six years old) were assessed for visual self-recognition ability, as well as for object permanence and gestural imitation. It was found that 13 of 15 autistic children showed evidence of self-recognition. Consistent relationships were suggested between self-cognition and object permanence but not between…

Textual emotion recognition for enhancing enterprise computing

Science.gov (United States)

Quan, Changqin; Ren, Fuji

2016-05-01

The growing interest in affective computing (AC) brings a lot of valuable research topics that can meet different application demands in enterprise systems. The present study explores a sub area of AC techniques - textual emotion recognition for enhancing enterprise computing. Multi-label emotion recognition in text is able to provide a more comprehensive understanding of emotions than single label emotion recognition. A representation of 'emotion state in text' is proposed to encompass the multidimensional emotions in text. It ensures the description in a formal way of the configurations of basic emotions as well as of the relations between them. Our method allows recognition of the emotions for the words bear indirect emotions, emotion ambiguity and multiple emotions. We further investigate the effect of word order for emotional expression by comparing the performances of bag-of-words model and sequence model for multi-label sentence emotion recognition. The experiments show that the classification results under sequence model are better than under bag-of-words model. And homogeneous Markov model showed promising results of multi-label sentence emotion recognition. This emotion recognition system is able to provide a convenient way to acquire valuable emotion information and to improve enterprise competitive ability in many aspects.

Window Size Impact in Human Activity Recognition

Directory of Open Access Journals (Sweden)

Oresti Banos

2014-04-01

Full Text Available Signal segmentation is a crucial stage in the activity recognition process; however, this has been rarely and vaguely characterized so far. Windowing approaches are normally used for segmentation, but no clear consensus exists on which window size should be preferably employed. In fact, most designs normally rely on figures used in previous works, but with no strict studies that support them. Intuitively, decreasing the window size allows for a faster activity detection, as well as reduced resources and energy needs. On the contrary, large data windows are normally considered for the recognition of complex activities. In this work, we present an extensive study to fairly characterize the windowing procedure, to determine its impact within the activity recognition process and to help clarify some of the habitual assumptions made during the recognition system design. To that end, some of the most widely used activity recognition procedures are evaluated for a wide range of window sizes and activities. From the evaluation, the interval 1–2 s proves to provide the best trade-off between recognition speed and accuracy. The study, specifically intended for on-body activity recognition systems, further provides designers with a set of guidelines devised to facilitate the system definition and configuration according to the particular application requirements and target activities.

Efficient Interaction Recognition through Positive Action Representation

Directory of Open Access Journals (Sweden)

Tao Hu

2013-01-01

Full Text Available This paper proposes a novel approach to decompose two-person interaction into a Positive Action and a Negative Action for more efficient behavior recognition. A Positive Action plays the decisive role in a two-person exchange. Thus, interaction recognition can be simplified to Positive Action-based recognition, focusing on an action representation of just one person. Recently, a new depth sensor has become widely available, the Microsoft Kinect camera, which provides RGB-D data with 3D spatial information for quantitative analysis. However, there are few publicly accessible test datasets using this camera, to assess two-person interaction recognition approaches. Therefore, we created a new dataset with six types of complex human interactions (i.e., named K3HI, including kicking, pointing, punching, pushing, exchanging an object, and shaking hands. Three types of features were extracted for each Positive Action: joint, plane, and velocity features. We used continuous Hidden Markov Models (HMMs to evaluate the Positive Action-based interaction recognition method and the traditional two-person interaction recognition approach with our test dataset. Experimental results showed that the proposed recognition technique is more accurate than the traditional method, shortens the sample training time, and therefore achieves comprehensive superiority.

Unvoiced Speech Recognition Using Tissue-Conductive Acoustic Sensor

Directory of Open Access Journals (Sweden)

Heracleous Panikos

2007-01-01

Full Text Available We present the use of stethoscope and silicon NAM (nonaudible murmur microphones in automatic speech recognition. NAM microphones are special acoustic sensors, which are attached behind the talker's ear and can capture not only normal (audible speech, but also very quietly uttered speech (nonaudible murmur. As a result, NAM microphones can be applied in automatic speech recognition systems when privacy is desired in human-machine communication. Moreover, NAM microphones show robustness against noise and they might be used in special systems (speech recognition, speech transform, etc. for sound-impaired people. Using adaptation techniques and a small amount of training data, we achieved for a 20 k dictation task a word accuracy for nonaudible murmur recognition in a clean environment. In this paper, we also investigate nonaudible murmur recognition in noisy environments and the effect of the Lombard reflex on nonaudible murmur recognition. We also propose three methods to integrate audible speech and nonaudible murmur recognition using a stethoscope NAM microphone with very promising results.

Face recognition based on improved BP neural network

Directory of Open Access Journals (Sweden)

Yue Gaili

2017-01-01

Full Text Available In order to improve the recognition rate of face recognition, face recognition algorithm based on histogram equalization, PCA and BP neural network is proposed. First, the face image is preprocessed by histogram equalization. Then, the classical PCA algorithm is used to extract the features of the histogram equalization image, and extract the principal component of the image. And then train the BP neural network using the trained training samples. This improved BP neural network weight adjustment method is used to train the network because the conventional BP algorithm has the disadvantages of slow convergence, easy to fall into local minima and training process. Finally, the BP neural network with the test sample input is trained to classify and identify the face images, and the recognition rate is obtained. Through the use of ORL database face image simulation experiment, the analysis results show that the improved BP neural network face recognition method can effectively improve the recognition rate of face recognition.

What affects the most to the recall and recognition of brand symbols?

OpenAIRE

Hasan, Syed Akif; Subhani, Muhammad Imtiaz; Osman, Ms. Amber

2012-01-01

Recall and Recognition, two important aspects when one talks about advertising and consumers awareness towards the brands. This study has mainly been focused on the brand symbols and its importance in the mind of the consumers. Does a brand symbol really play a role of differentiating a brand from another and how well it gets associated with the consumer? Questions like these are well-answered in this research. 250 respondents from the largest city of Pakistan i.e. Karachi were handed over a ...

Emotion recognition in Chinese people with schizophrenia.

Science.gov (United States)

Chan, Chetwyn C H; Wong, Raymond; Wang, Kai; Lee, Tatia M C

2008-01-15

This study examined whether people with paranoid or nonparanoid schizophrenia would show emotion-recognition deficits, both facial and prosodic. Furthermore, this study examined the neuropsychological predictors of emotion-recognition ability in people with schizophrenia. Participants comprised 86 people, of whom: 43 were people diagnosed with schizophrenia and 43 were controls. The 43 clinical participants were placed in either the paranoid group (n=19) or the nonparanoid group (n=24). Each participant was administered the Facial Emotion Recognition task and the Prosodic Recognition task, together with other neuropsychological measures of attention and visual perception. People suffering from nonparanoid schizophrenia were found to have deficits in both facial and prosodic emotion recognition, after correction for the differences in the intelligence and depression scores between the two groups. Furthermore, spatial perception was observed to be the best predictor of facial emotion identification in individuals with nonparanoid schizophrenia, whereas attentional processing control predicted both prosodic emotion identification and discrimination in nonparanoid schizophrenia patients. Our findings suggest that patients with schizophrenia in remission may still suffer from impairment of certain aspects of emotion recognition.

Implicit Recognition Based on Lateralized Perceptual Fluency

Directory of Open Access Journals (Sweden)

Iliana M. Vargas

2012-02-01

Full Text Available In some circumstances, accurate recognition of repeated images in an explicit memory test is driven by implicit memory. We propose that this “implicit recognition” results from perceptual fluency that influences responding without awareness of memory retrieval. Here we examined whether recognition would vary if images appeared in the same or different visual hemifield during learning and testing. Kaleidoscope images were briefly presented left or right of fixation during divided-attention encoding. Presentation in the same visual hemifield at test produced higher recognition accuracy than presentation in the opposite visual hemifield, but only for guess responses. These correct guesses likely reflect a contribution from implicit recognition, given that when the stimulated visual hemifield was the same at study and test, recognition accuracy was higher for guess responses than for responses with any level of confidence. The dramatic difference in guessing accuracy as a function of lateralized perceptual overlap between study and test suggests that implicit recognition arises from memory storage in visual cortical networks that mediate repetition-induced fluency increments.

Dynamic Programming Algorithms in Speech Recognition

Directory of Open Access Journals (Sweden)

Titus Felix FURTUNA

2008-01-01

Full Text Available In a system of speech recognition containing words, the recognition requires the comparison between the entry signal of the word and the various words of the dictionary. The problem can be solved efficiently by a dynamic comparison algorithm whose goal is to put in optimal correspondence the temporal scales of the two words. An algorithm of this type is Dynamic Time Warping. This paper presents two alternatives for implementation of the algorithm designed for recognition of the isolated words.

Cortical Networks for Visual Self-Recognition

Science.gov (United States)

Sugiura, Motoaki

This paper briefly reviews recent developments regarding the brain mechanisms of visual self-recognition. A special cognitive mechanism for visual self-recognition has been postulated based on behavioral and neuropsychological evidence, but its neural substrate remains controversial. Recent functional imaging studies suggest that multiple cortical mechanisms play self-specific roles during visual self-recognition, reconciling the existing controversy. Respective roles for the left occipitotemporal, right parietal, and frontal cortices in symbolic, visuospatial, and conceptual aspects of self-representation have been proposed.

Cortical networks for visual self-recognition

International Nuclear Information System (INIS)

Sugiura, Motoaki

2007-01-01

This paper briefly reviews recent developments regarding the brain mechanisms of visual self-recognition. A special cognitive mechanism for visual self-recognition has been postulated based on behavioral and neuropsychological evidence, but its neural substrate remains controversial. Recent functional imaging studies suggest that multiple cortical mechanisms play self-specific roles during visual self-recognition, reconciling the existing controversy. Respective roles for the left occipitotemporal, right parietal, and frontal cortices in symbolic, visuospatial, and conceptual aspects of self-representation have been proposed. (author)

The role of the hippocampus in recognition memory.

Science.gov (United States)

Bird, Chris M

2017-08-01

Many theories of declarative memory propose that it is supported by partially separable processes underpinned by different brain structures. The hippocampus plays a critical role in binding together item and contextual information together and processing the relationships between individual items. By contrast, the processing of individual items and their later recognition can be supported by extrahippocampal regions of the medial temporal lobes (MTL), particularly when recognition is based on feelings of familiarity without the retrieval of any associated information. These theories are domain-general in that "items" might be words, faces, objects, scenes, etc. However, there is mixed evidence that item recognition does not require the hippocampus, or that familiarity-based recognition can be supported by extrahippocampal regions. By contrast, there is compelling evidence that in humans, hippocampal damage does not affect recognition memory for unfamiliar faces, whilst recognition memory for several other stimulus classes is impaired. I propose that regions outside of the hippocampus can support recognition of unfamiliar faces because they are perceived as discrete items and have no prior conceptual associations. Conversely, extrahippocampal processes are inadequate for recognition of items which (a) have been previously experienced, (b) are conceptually meaningful, or (c) are perceived as being comprised of individual elements. This account reconciles findings from primate and human studies of recognition memory. Furthermore, it suggests that while the hippocampus is critical for binding and relational processing, these processes are required for item recognition memory in most situations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Face recognition : implementation of face recognition on AMIGO

NARCIS (Netherlands)

Geelen, M.J.A.J.; Molengraft, van de M.J.G.; Elfring, J.

2011-01-01

In this (traineeship)report two possible methods of face recognition were presented. The first method describes how to detect and recognize faces by using the SURF algorithm. This algorithm finally was not used for recognizing faces, with the reason that the Eigenface algorithm was an already tested

Automated target recognition and tracking using an optical pattern recognition neural network

Science.gov (United States)

Chao, Tien-Hsin

1991-01-01

The on-going development of an automatic target recognition and tracking system at the Jet Propulsion Laboratory is presented. This system is an optical pattern recognition neural network (OPRNN) that is an integration of an innovative optical parallel processor and a feature extraction based neural net training algorithm. The parallel optical processor provides high speed and vast parallelism as well as full shift invariance. The neural network algorithm enables simultaneous discrimination of multiple noisy targets in spite of their scales, rotations, perspectives, and various deformations. This fully developed OPRNN system can be effectively utilized for the automated spacecraft recognition and tracking that will lead to success in the Automated Rendezvous and Capture (AR&C) of the unmanned Cargo Transfer Vehicle (CTV). One of the most powerful optical parallel processors for automatic target recognition is the multichannel correlator. With the inherent advantages of parallel processing capability and shift invariance, multiple objects can be simultaneously recognized and tracked using this multichannel correlator. This target tracking capability can be greatly enhanced by utilizing a powerful feature extraction based neural network training algorithm such as the neocognitron. The OPRNN, currently under investigation at JPL, is constructed with an optical multichannel correlator where holographic filters have been prepared using the neocognitron training algorithm. The computation speed of the neocognitron-type OPRNN is up to 10(exp 14) analog connections/sec that enabling the OPRNN to outperform its state-of-the-art electronics counterpart by at least two orders of magnitude.

Image-based automatic recognition of larvae

Science.gov (United States)

Sang, Ru; Yu, Guiying; Fan, Weijun; Guo, Tiantai

2010-08-01

As the main objects, imagoes have been researched in quarantine pest recognition in these days. However, pests in their larval stage are latent, and the larvae spread abroad much easily with the circulation of agricultural and forest products. It is presented in this paper that, as the new research objects, larvae are recognized by means of machine vision, image processing and pattern recognition. More visional information is reserved and the recognition rate is improved as color image segmentation is applied to images of larvae. Along with the characteristics of affine invariance, perspective invariance and brightness invariance, scale invariant feature transform (SIFT) is adopted for the feature extraction. The neural network algorithm is utilized for pattern recognition, and the automatic identification of larvae images is successfully achieved with satisfactory results.

Temporal visual cues aid speech recognition

DEFF Research Database (Denmark)

Zhou, Xiang; Ross, Lars; Lehn-Schiøler, Tue

2006-01-01

of audio to generate an artificial talking-face video and measured word recognition performance on simple monosyllabic words. RESULTS: When presenting words together with the artificial video we find that word recognition is improved over purely auditory presentation. The effect is significant (p......BACKGROUND: It is well known that under noisy conditions, viewing a speaker's articulatory movement aids the recognition of spoken words. Conventionally it is thought that the visual input disambiguates otherwise confusing auditory input. HYPOTHESIS: In contrast we hypothesize...... that it is the temporal synchronicity of the visual input that aids parsing of the auditory stream. More specifically, we expected that purely temporal information, which does not convey information such as place of articulation may facility word recognition. METHODS: To test this prediction we used temporal features...

Accurate forced-choice recognition without awareness of memory retrieval

OpenAIRE

Voss, Joel L.; Baym, Carol L.; Paller, Ken A.

2008-01-01

Recognition confidence and the explicit awareness of memory retrieval commonly accompany accurate responding in recognition tests. Memory performance in recognition tests is widely assumed to measure explicit memory, but the generality of this assumption is questionable. Indeed, whether recognition in nonhumans is always supported by explicit memory is highly controversial. Here we identified circumstances wherein highly accurate recognition was unaccompanied by hallmark features of explicit ...

Infants' Recognition Memory for Hue

Science.gov (United States)

Bornstein, Marc H.

1976-01-01

Fifty 4-month-old infants were habituated to one wavelength of light and then tested for recognition with the original and two new spectral lights. After short- and long-term delays with different types of retroactive interference, the results indicated that the infants' recognition memory for hue was quite resilient to interference or delay. (JMB)

Does cortisol modulate emotion recognition and empathy?

Science.gov (United States)

Duesenberg, Moritz; Weber, Juliane; Schulze, Lars; Schaeuffele, Carmen; Roepke, Stefan; Hellmann-Regen, Julian; Otte, Christian; Wingenfeld, Katja

2016-04-01

Emotion recognition and empathy are important aspects in the interaction and understanding of other people's behaviors and feelings. The Human environment comprises of stressful situations that impact social interactions on a daily basis. Aim of the study was to examine the effects of the stress hormone cortisol on emotion recognition and empathy. In this placebo-controlled study, 40 healthy men and 40 healthy women (mean age 24.5 years) received either 10mg of hydrocortisone or placebo. We used the Multifaceted Empathy Test to measure emotional and cognitive empathy. Furthermore, we examined emotion recognition from facial expressions, which contained two emotions (anger and sadness) and two emotion intensities (40% and 80%). We did not find a main effect for treatment or sex on either empathy or emotion recognition but a sex × emotion interaction on emotion recognition. The main result was a four-way-interaction on emotion recognition including treatment, sex, emotion and task difficulty. At 40% task difficulty, women recognized angry faces better than men in the placebo condition. Furthermore, in the placebo condition, men recognized sadness better than anger. At 80% task difficulty, men and women performed equally well in recognizing sad faces but men performed worse compared to women with regard to angry faces. Apparently, our results did not support the hypothesis that increases in cortisol concentration alone influence empathy and emotion recognition in healthy young individuals. However, sex and task difficulty appear to be important variables in emotion recognition from facial expressions. Copyright © 2016 Elsevier Ltd. All rights reserved.

Electrophysiological distinctions between recognition memory with and without awareness

Science.gov (United States)

Ko, Philip C.; Duda, Bryant; Hussey, Erin P.; Ally, Brandon A.

2013-01-01

The influence of implicit memory representations on explicit recognition may help to explain cases of accurate recognition decisions made with high uncertainty. During a recognition task, implicit memory may enhance the fluency of a test item, biasing decision processes to endorse it as “old”. This model may help explain recognition-without-identification, a remarkable phenomenon in which participants make highly accurate recognition decisions despite the inability to identify the test item. The current study investigated whether recognition-without-identification for pictures elicits a similar pattern of neural activity as other types of accurate recognition decisions made with uncertainty. Further, this study also examined whether recognition-without-identification for pictures could be attained by the use of perceptual and conceptual information from memory. To accomplish this, participants studied pictures and then performed a recognition task under difficult viewing conditions while event-related potentials (ERPs) were recorded. Behavioral results showed that recognition was highly accurate even when test items could not be identified, demonstrating recognition-without identification. The behavioral performance also indicated that recognition-without-identification was mediated by both perceptual and conceptual information, independently of one another. The ERP results showed dramatically different memory related activity during the early 300 to 500 ms epoch for identified items that were studied compared to unidentified items that were studied. Similar to previous work highlighting accurate recognition without retrieval awareness, test items that were not identified, but correctly endorsed as “old,” elicited a negative posterior old/new effect (i.e., N300). In contrast, test items that were identified and correctly endorsed as “old,” elicited the classic positive frontal old/new effect (i.e., FN400). Importantly, both of these effects were

Gender recognition from unconstrained and articulated human body.

Science.gov (United States)

Wu, Qin; Guo, Guodong

2014-01-01

Gender recognition has many useful applications, ranging from business intelligence to image search and social activity analysis. Traditional research on gender recognition focuses on face images in a constrained environment. This paper proposes a method for gender recognition in articulated human body images acquired from an unconstrained environment in the real world. A systematic study of some critical issues in body-based gender recognition, such as which body parts are informative, how many body parts are needed to combine together, and what representations are good for articulated body-based gender recognition, is also presented. This paper also pursues data fusion schemes and efficient feature dimensionality reduction based on the partial least squares estimation. Extensive experiments are performed on two unconstrained databases which have not been explored before for gender recognition.

Gender Recognition from Unconstrained and Articulated Human Body

Science.gov (United States)

Wu, Qin; Guo, Guodong

2014-01-01

Gender recognition has many useful applications, ranging from business intelligence to image search and social activity analysis. Traditional research on gender recognition focuses on face images in a constrained environment. This paper proposes a method for gender recognition in articulated human body images acquired from an unconstrained environment in the real world. A systematic study of some critical issues in body-based gender recognition, such as which body parts are informative, how many body parts are needed to combine together, and what representations are good for articulated body-based gender recognition, is also presented. This paper also pursues data fusion schemes and efficient feature dimensionality reduction based on the partial least squares estimation. Extensive experiments are performed on two unconstrained databases which have not been explored before for gender recognition. PMID:24977203

Repetition and lag effects in movement recognition.

Science.gov (United States)

Hall, C R; Buckolz, E

1982-03-01

Whether repetition and lag improve the recognition of movement patterns was investigated. Recognition memory was tested for one repetition, two-repetitions massed, and two-repetitions distributed with movement patterns at lags of 3, 5, 7, and 13. Recognition performance was examined both immediately afterwards and following a 48 hour delay. Both repetition and lag effects failed to be demonstrated, providing some support for the claim that memory is unaffected by repetition at a constant level of processing (Craik & Lockhart, 1972). There was, as expected, a significant decrease in recognition memory following the retention interval, but this appeared unrelated to repetition or lag.

Robust recognition via information theoretic learning

CERN Document Server

He, Ran; Yuan, Xiaotong; Wang, Liang

2014-01-01

This Springer Brief represents a comprehensive review of information theoretic methods for robust recognition. A variety of information theoretic methods have been proffered in the past decade, in a large variety of computer vision applications; this work brings them together, attempts to impart the theory, optimization and usage of information entropy.The?authors?resort to a new information theoretic concept, correntropy, as a robust measure and apply it to solve robust face recognition and object recognition problems. For computational efficiency,?the brief?introduces the additive and multip

Acoustic Pattern Recognition on Android Devices

DEFF Research Database (Denmark)

Møller, Maiken Bjerg; Gaarsdal, Jesper; Steen, Kim Arild

2013-01-01

an Android application developed for acoustic pattern recognition of bird species. The acoustic data is recorded using a built-in microphone, and pattern recognition is performed on the device, requiring no network connection. The algorithm is implemented in C++ as a native Android module and the Open......CV library is used for signal processing. We conclude that the approach presented here is a viable solution to pattern recognition problems. Since it requires no network connection, it shows promise in fields such as wildlife research....

Evaluating music emotion recognition

DEFF Research Database (Denmark)

Sturm, Bob L.

2013-01-01

A fundamental problem with nearly all work in music genre recognition (MGR)is that evaluation lacks validity with respect to the principal goals of MGR. This problem also occurs in the evaluation of music emotion recognition (MER). Standard approaches to evaluation, though easy to implement, do...... not reliably differentiate between recognizing genre or emotion from music, or by virtue of confounding factors in signals (e.g., equalization). We demonstrate such problems for evaluating an MER system, and conclude with recommendations....

Automatic speech recognition (zero crossing method). Automatic recognition of isolated vowels

International Nuclear Information System (INIS)

Dupeyrat, Benoit

1975-01-01

This note describes a recognition method of isolated vowels, using a preprocessing of the vocal signal. The processing extracts the extrema of the vocal signal and the interval time separating them (Zero crossing distances of the first derivative of the signal). The recognition of vowels uses normalized histograms of the values of these intervals. The program determines a distance between the histogram of the sound to be recognized and histograms models built during a learning phase. The results processed on real time by a minicomputer, are relatively independent of the speaker, the fundamental frequency being not allowed to vary too much (i.e. speakers of the same sex). (author) [fr

Star pattern recognition algorithm aided by inertial information

Science.gov (United States)

Liu, Bao; Wang, Ke-dong; Zhang, Chao

2011-08-01

Star pattern recognition is one of the key problems of the celestial navigation. The traditional star pattern recognition approaches, such as the triangle algorithm and the star angular distance algorithm, are a kind of all-sky matching method whose recognition speed is slow and recognition success rate is not high. Therefore, the real time and reliability of CNS (Celestial Navigation System) is reduced to some extent, especially for the maneuvering spacecraft. However, if the direction of the camera optical axis can be estimated by other navigation systems such as INS (Inertial Navigation System), the star pattern recognition can be fulfilled in the vicinity of the estimated direction of the optical axis. The benefits of the INS-aided star pattern recognition algorithm include at least the improved matching speed and the improved success rate. In this paper, the direction of the camera optical axis, the local matching sky, and the projection of stars on the image plane are estimated by the aiding of INS firstly. Then, the local star catalog for the star pattern recognition is established in real time dynamically. The star images extracted in the camera plane are matched in the local sky. Compared to the traditional all-sky star pattern recognition algorithms, the memory of storing the star catalog is reduced significantly. Finally, the INS-aided star pattern recognition algorithm is validated by simulations. The results of simulations show that the algorithm's computation time is reduced sharply and its matching success rate is improved greatly.

Image preprocessing study on KPCA-based face recognition

Science.gov (United States)

Li, Xuan; Li, Dehua

2015-12-01

Face recognition as an important biometric identification method, with its friendly, natural, convenient advantages, has obtained more and more attention. This paper intends to research a face recognition system including face detection, feature extraction and face recognition, mainly through researching on related theory and the key technology of various preprocessing methods in face detection process, using KPCA method, focuses on the different recognition results in different preprocessing methods. In this paper, we choose YCbCr color space for skin segmentation and choose integral projection for face location. We use erosion and dilation of the opening and closing operation and illumination compensation method to preprocess face images, and then use the face recognition method based on kernel principal component analysis method for analysis and research, and the experiments were carried out using the typical face database. The algorithms experiment on MATLAB platform. Experimental results show that integration of the kernel method based on PCA algorithm under certain conditions make the extracted features represent the original image information better for using nonlinear feature extraction method, which can obtain higher recognition rate. In the image preprocessing stage, we found that images under various operations may appear different results, so as to obtain different recognition rate in recognition stage. At the same time, in the process of the kernel principal component analysis, the value of the power of the polynomial function can affect the recognition result.

Misattribution, false recognition and the sins of memory.

Science.gov (United States)

Schacter, D L; Dodson, C S

2001-09-29

Memory is sometimes a troublemaker. Schacter has classified memory's transgressions into seven fundamental 'sins': transience, absent-mindedness, blocking, misattribution, suggestibility, bias and persistence. This paper focuses on one memory sin, misattribution, that is implicated in false or illusory recognition of episodes that never occurred. We present data from cognitive, neuropsychological and neuroimaging studies that illuminate aspects of misattribution and false recognition. We first discuss cognitive research examining possible mechanisms of misattribution associated with false recognition. We also consider ways in which false recognition can be reduced or avoided, focusing in particular on the role of distinctive information. We next turn to neuropsychological research concerning patients with amnesia and Alzheimer's disease that reveals conditions under which such patients are less susceptible to false recognition than are healthy controls, thus providing clues about the brain mechanisms that drive false recognition. We then consider neuroimaging studies concerned with the neural correlates of true and false recognition, examining when the two forms of recognition can and cannot be distinguished on the basis of brain activity. Finally, we argue that even though misattribution and other memory sins are annoying and even dangerous, they can also be viewed as by-products of adaptive features of memory.

Unvoiced Speech Recognition Using Tissue-Conductive Acoustic Sensor

Directory of Open Access Journals (Sweden)

Hiroshi Saruwatari

2007-01-01

Full Text Available We present the use of stethoscope and silicon NAM (nonaudible murmur microphones in automatic speech recognition. NAM microphones are special acoustic sensors, which are attached behind the talker's ear and can capture not only normal (audible speech, but also very quietly uttered speech (nonaudible murmur. As a result, NAM microphones can be applied in automatic speech recognition systems when privacy is desired in human-machine communication. Moreover, NAM microphones show robustness against noise and they might be used in special systems (speech recognition, speech transform, etc. for sound-impaired people. Using adaptation techniques and a small amount of training data, we achieved for a 20 k dictation task a 93.9% word accuracy for nonaudible murmur recognition in a clean environment. In this paper, we also investigate nonaudible murmur recognition in noisy environments and the effect of the Lombard reflex on nonaudible murmur recognition. We also propose three methods to integrate audible speech and nonaudible murmur recognition using a stethoscope NAM microphone with very promising results.

Eye movements during object recognition in visual agnosia.

Science.gov (United States)

Charles Leek, E; Patterson, Candy; Paul, Matthew A; Rafal, Robert; Cristino, Filipe

2012-07-01

This paper reports the first ever detailed study about eye movement patterns during single object recognition in visual agnosia. Eye movements were recorded in a patient with an integrative agnosic deficit during two recognition tasks: common object naming and novel object recognition memory. The patient showed normal directional biases in saccades and fixation dwell times in both tasks and was as likely as controls to fixate within object bounding contour regardless of recognition accuracy. In contrast, following initial saccades of similar amplitude to controls, the patient showed a bias for short saccades. In object naming, but not in recognition memory, the similarity of the spatial distributions of patient and control fixations was modulated by recognition accuracy. The study provides new evidence about how eye movements can be used to elucidate the functional impairments underlying object recognition deficits. We argue that the results reflect a breakdown in normal functional processes involved in the integration of shape information across object structure during the visual perception of shape. Copyright © 2012 Elsevier Ltd. All rights reserved.

Impaired emotion recognition in music in Parkinson's disease.

Science.gov (United States)

van Tricht, Mirjam J; Smeding, Harriet M M; Speelman, Johannes D; Schmand, Ben A

2010-10-01

Music has the potential to evoke strong emotions and plays a significant role in the lives of many people. Music might therefore be an ideal medium to assess emotion recognition. We investigated emotion recognition in music in 20 patients with idiopathic Parkinson's disease (PD) and 20 matched healthy volunteers. The role of cognitive dysfunction and other disease characteristics in emotion recognition was also evaluated. We used 32 musical excerpts that expressed happiness, sadness, fear or anger. PD patients were impaired in recognizing fear and anger in music. Fear recognition was associated with executive functions in PD patients and in healthy controls, but the emotion recognition impairments of PD patients persisted after adjusting for executive functioning. We found no differences in the recognition of happy or sad music. Emotion recognition was not related to depressive symptoms, disease duration or severity of motor symptoms. We conclude that PD patients are impaired in recognizing complex emotions in music. Although this impairment is related to executive dysfunction, our findings most likely reflect an additional primary deficit in emotional processing. 2010 Elsevier Inc. All rights reserved.

Hybrid methodological approach to context-dependent speech recognition

Directory of Open Access Journals (Sweden)

Dragiša Mišković

2017-01-01

Full Text Available Although the importance of contextual information in speech recognition has been acknowledged for a long time now, it has remained clearly underutilized even in state-of-the-art speech recognition systems. This article introduces a novel, methodologically hybrid approach to the research question of context-dependent speech recognition in human–machine interaction. To the extent that it is hybrid, the approach integrates aspects of both statistical and representational paradigms. We extend the standard statistical pattern-matching approach with a cognitively inspired and analytically tractable model with explanatory power. This methodological extension allows for accounting for contextual information which is otherwise unavailable in speech recognition systems, and using it to improve post-processing of recognition hypotheses. The article introduces an algorithm for evaluation of recognition hypotheses, illustrates it for concrete interaction domains, and discusses its implementation within two prototype conversational agents.

Vehicle logo recognition using multi-level fusion model

Science.gov (United States)

Ming, Wei; Xiao, Jianli

2018-04-01

Vehicle logo recognition plays an important role in manufacturer identification and vehicle recognition. This paper proposes a new vehicle logo recognition algorithm. It has a hierarchical framework, which consists of two fusion levels. At the first level, a feature fusion model is employed to map the original features to a higher dimension feature space. In this space, the vehicle logos become more recognizable. At the second level, a weighted voting strategy is proposed to promote the accuracy and the robustness of the recognition results. To evaluate the performance of the proposed algorithm, extensive experiments are performed, which demonstrate that the proposed algorithm can achieve high recognition accuracy and work robustly.

Contemporary deep recurrent learning for recognition

Science.gov (United States)

Iftekharuddin, K. M.; Alam, M.; Vidyaratne, L.

2017-05-01

Large-scale feed-forward neural networks have seen intense application in many computer vision problems. However, these networks can get hefty and computationally intensive with increasing complexity of the task. Our work, for the first time in literature, introduces a Cellular Simultaneous Recurrent Network (CSRN) based hierarchical neural network for object detection. CSRN has shown to be more effective to solving complex tasks such as maze traversal and image processing when compared to generic feed forward networks. While deep neural networks (DNN) have exhibited excellent performance in object detection and recognition, such hierarchical structure has largely been absent in neural networks with recurrency. Further, our work introduces deep hierarchy in SRN for object recognition. The simultaneous recurrency results in an unfolding effect of the SRN through time, potentially enabling the design of an arbitrarily deep network. This paper shows experiments using face, facial expression and character recognition tasks using novel deep recurrent model and compares recognition performance with that of generic deep feed forward model. Finally, we demonstrate the flexibility of incorporating our proposed deep SRN based recognition framework in a humanoid robotic platform called NAO.

Can corrective feedback improve recognition memory?

Science.gov (United States)

Kantner, Justin; Lindsay, D Stephen

2010-06-01

An understanding of the effects of corrective feedback on recognition memory can inform both recognition theory and memory training programs, but few published studies have investigated the issue. Although the evidence to date suggests that feedback does not improve recognition accuracy, few studies have directly examined its effect on sensitivity, and fewer have created conditions that facilitate a feedback advantage by encouraging controlled processing at test. In Experiment 1, null effects of feedback were observed following both deep and shallow encoding of categorized study lists. In Experiment 2, feedback robustly influenced response bias by allowing participants to discern highly uneven base rates of old and new items, but sensitivity remained unaffected. In Experiment 3, a false-memory procedure, feedback failed to attenuate false recognition of critical lures. In Experiment 4, participants were unable to use feedback to learn a simple category rule separating old items from new items, despite the fact that feedback was of substantial benefit in a nearly identical categorization task. The recognition system, despite a documented ability to utilize controlled strategic or inferential decision-making processes, appears largely impenetrable to a benefit of corrective feedback.

How fast is famous face recognition?

Directory of Open Access Journals (Sweden)

Gladys eBarragan-Jason

2012-10-01

Full Text Available The rapid recognition of familiar faces is crucial for social interactions. However the actual speed with which recognition can be achieved remains largely unknown as most studies have been carried out without any speed constraints. Different paradigms have been used, leading to conflicting results, and although many authors suggest that face recognition is fast, the speed of face recognition has not been directly compared to fast visual tasks. In this study, we sought to overcome these limitations. Subjects performed three tasks, a familiarity categorization task (famous faces among unknown faces, a superordinate categorization task (human faces among animal ones and a gender categorization task. All tasks were performed under speed constraints. The results show that, despite the use of speed constraints, subjects were slow when they had to categorize famous faces: minimum reaction time was 467 ms, which is 180 ms more than during superordinate categorization and 160 ms more than in the gender condition. Our results are compatible with a hierarchy of face processing from the superordinate level to the familiarity level. The processes taking place between detection and recognition need to be investigated in detail.

Learning and recognition of on-premise signs from weakly labeled street view images.

Science.gov (United States)

Tsai, Tsung-Hung; Cheng, Wen-Huang; You, Chuang-Wen; Hu, Min-Chun; Tsui, Arvin Wen; Chi, Heng-Yu

2014-03-01

Camera-enabled mobile devices are commonly used as interaction platforms for linking the user's virtual and physical worlds in numerous research and commercial applications, such as serving an augmented reality interface for mobile information retrieval. The various application scenarios give rise to a key technique of daily life visual object recognition. On-premise signs (OPSs), a popular form of commercial advertising, are widely used in our living life. The OPSs often exhibit great visual diversity (e.g., appearing in arbitrary size), accompanied with complex environmental conditions (e.g., foreground and background clutter). Observing that such real-world characteristics are lacking in most of the existing image data sets, in this paper, we first proposed an OPS data set, namely OPS-62, in which totally 4649 OPS images of 62 different businesses are collected from Google's Street View. Further, for addressing the problem of real-world OPS learning and recognition, we developed a probabilistic framework based on the distributional clustering, in which we proposed to exploit the distributional information of each visual feature (the distribution of its associated OPS labels) as a reliable selection criterion for building discriminative OPS models. Experiments on the OPS-62 data set demonstrated the outperformance of our approach over the state-of-the-art probabilistic latent semantic analysis models for more accurate recognitions and less false alarms, with a significant 151.28% relative improvement in the average recognition rate. Meanwhile, our approach is simple, linear, and can be executed in a parallel fashion, making it practical and scalable for large-scale multimedia applications.

Gender Recognition from Unconstrained and Articulated Human Body

OpenAIRE

Wu, Qin; Guo, Guodong

2014-01-01

Gender recognition has many useful applications, ranging from business intelligence to image search and social activity analysis. Traditional research on gender recognition focuses on face images in a constrained environment. This paper proposes a method for gender recognition in articulated human body images acquired from an unconstrained environment in the real world. A systematic study of some critical issues in body-based gender recognition, such as which body parts are informative, ho...

Kin-informative recognition cues in ants

DEFF Research Database (Denmark)

Nehring, Volker; Evison, Sophie E F; Santorelli, Lorenzo A

2011-01-01

behaviour is thought to be rare in one of the classic examples of cooperation--social insect colonies--because the colony-level costs of individual selfishness select against cues that would allow workers to recognize their closest relatives. In accord with this, previous studies of wasps and ants have...... found little or no kin information in recognition cues. Here, we test the hypothesis that social insects do not have kin-informative recognition cues by investigating the recognition cues and relatedness of workers from four colonies of the ant Acromyrmex octospinosus. Contrary to the theoretical...... prediction, we show that the cuticular hydrocarbons of ant workers in all four colonies are informative enough to allow full-sisters to be distinguished from half-sisters with a high accuracy. These results contradict the hypothesis of non-heritable recognition cues and suggest that there is more potential...

Mediators and moderators of magazine advertisement effects on adolescent cigarette smoking.

Science.gov (United States)

Aloise-Young, Patricia A; Slater, Michael D; Cruickshank, Courtney C

2006-01-01

The purpose of the present study is to examine the relation between magazine advertising for cigarettes and adolescent cigarette smoking. Participants (242 adolescents) reported their frequency of reading 46 magazines and their attention to cigarette ads. Recognition of cigarette ads, passive peer pressure (i.e., normative beliefs), and the smoker image also were assessed. Results indicate that exposure to cigarette advertising and recognition of ads augment the effect of passive peer pressure on smoking. In addition, a positive smoker image was associated with attention to advertising and mediated the relation between attention and smoking. It is suggested that the effect of magazine ads on adolescents should be considered in policymaking on cigarette advertising.

Covert face recognition in congenital prosopagnosia: a group study.

Science.gov (United States)

Rivolta, Davide; Palermo, Romina; Schmalzl, Laura; Coltheart, Max

2012-03-01

Even though people with congenital prosopagnosia (CP) never develop a normal ability to "overtly" recognize faces, some individuals show indices of "covert" (or implicit) face recognition. The aim of this study was to demonstrate covert face recognition in CP when participants could not overtly recognize the faces. Eleven people with CP completed three tasks assessing their overt face recognition ability, and three tasks assessing their "covert" face recognition: a Forced choice familiarity task, a Forced choice cued task, and a Priming task. Evidence of covert recognition was observed with the Forced choice familiarity task, but not the Priming task. In addition, we propose that the Forced choice cued task does not measure covert processing as such, but instead "provoked-overt" recognition. Our study clearly shows that people with CP demonstrate covert recognition for faces that they cannot overtly recognize, and that behavioural tasks vary in their sensitivity to detect covert recognition in CP. Copyright © 2011 Elsevier Srl. All rights reserved.

Participation, Recognition and the Democratic Doxa

DEFF Research Database (Denmark)

Harrits, Gitte Sommer

2006-01-01

to the exclusionary effects of norms of citizenship, i.e. the exclusionfrom within, and suggest the recognition of group differences. This paper tries to suggest, how a Bourdieu-perspective can help bridge the gap of dichotomies such as individual/group, universalism/particularism and rights/recognition. The paper...... suggest that a democratisation of the political doxa, involving the recognition of differences in political habitus and (most importantly) practices is necessary to oppose the tendencies of exclusion and to further a widespread empowerment of citizens in late modern societies, without this turing...

Gait recognition based on integral outline

Science.gov (United States)

Ming, Guan; Fang, Lv

2017-02-01

Biometric identification technology replaces traditional security technology, which has become a trend, and gait recognition also has become a hot spot of research because its feature is difficult to imitate and theft. This paper presents a gait recognition system based on integral outline of human body. The system has three important aspects: the preprocessing of gait image, feature extraction and classification. Finally, using a method of polling to evaluate the performance of the system, and summarizing the problems existing in the gait recognition and the direction of development in the future.

Pattern recognition and classification an introduction

CERN Document Server

Dougherty, Geoff

2012-01-01

The use of pattern recognition and classification is fundamental to many of the automated electronic systems in use today. However, despite the existence of a number of notable books in the field, the subject remains very challenging, especially for the beginner. Pattern Recognition and Classification presents a comprehensive introduction to the core concepts involved in automated pattern recognition. It is designed to be accessible to newcomers from varied backgrounds, but it will also be useful to researchers and professionals in image and signal processing and analysis, and in computer visi

Activity recognition from minimal distinguishing subsequence mining

Science.gov (United States)

Iqbal, Mohammad; Pao, Hsing-Kuo

2017-08-01

Human activity recognition is one of the most important research topics in the era of Internet of Things. To separate different activities given sensory data, we utilize a Minimal Distinguishing Subsequence (MDS) mining approach to efficiently find distinguishing patterns among different activities. We first transform the sensory data into a series of sensor triggering events and operate the MDS mining procedure afterwards. The gap constraints are also considered in the MDS mining. Given the multi-class nature of most activity recognition tasks, we modify the MDS mining approach from a binary case to a multi-class one to fit the need for multiple activity recognition. We also study how to select the best parameter set including the minimal and the maximal support thresholds in finding the MDSs for effective activity recognition. Overall, the prediction accuracy is 86.59% on the van Kasteren dataset which consists of four different activities for recognition.

Man machine interface based on speech recognition

International Nuclear Information System (INIS)

Jorge, Carlos A.F.; Aghina, Mauricio A.C.; Mol, Antonio C.A.; Pereira, Claudio M.N.A.

2007-01-01

This work reports the development of a Man Machine Interface based on speech recognition. The system must recognize spoken commands, and execute the desired tasks, without manual interventions of operators. The range of applications goes from the execution of commands in an industrial plant's control room, to navigation and interaction in virtual environments. Results are reported for isolated word recognition, the isolated words corresponding to the spoken commands. For the pre-processing stage, relevant parameters are extracted from the speech signals, using the cepstral analysis technique, that are used for isolated word recognition, and corresponds to the inputs of an artificial neural network, that performs recognition tasks. (author)

Infants' Delayed Recognition Memory and Forgetting

Science.gov (United States)

Fagan, Joseph F., III

1973-01-01

Infants 21- to 25-weeks-old devoted more visual fixation to novel than familiar stimuli on immediate and delayed recognition tests. The experiments confirm the existence of long-term recognition memory for pictorial stimuli in the early months of life. (DP)

The recognition heuristic : A review of theory and tests

OpenAIRE

Pachur, T.; Todd, P.; Gigerenzer, G.; Schooler, L.; Goldstein, D.

2011-01-01

The recognition heuristic is a prime example of how, by exploiting a match between mind and environment, a simple mental strategy can lead to efficient decision making. The proposal of the heuristic initiated a debate about the processes underlying the use of recognition in decision making. We review research addressing four key aspects of the recognition heuristic: (a) that recognition is often an ecologically valid cue; (b) that people often follow recognition when making inferences; (c) th...

Touchless palmprint recognition systems

CERN Document Server

Genovese, Angelo; Scotti, Fabio

2014-01-01

This book examines the context, motivation and current status of biometric systems based on the palmprint, with a specific focus on touchless and less-constrained systems. It covers new technologies in this rapidly evolving field and is one of the first comprehensive books on palmprint recognition systems.It discusses the research literature and the most relevant industrial applications of palmprint biometrics, including the low-cost solutions based on webcams. The steps of biometric recognition are described in detail, including acquisition setups, algorithms, and evaluation procedures. Const

Multi-thread Parallel Speech Recognition for Mobile Applications

Directory of Open Access Journals (Sweden)

LOJKA Martin

2014-05-01

Full Text Available In this paper, the server based solution of the multi-thread large vocabulary automatic speech recognition engine is described along with the Android OS and HTML5 practical application examples. The basic idea was to bring speech recognition available for full variety of applications for computers and especially for mobile devices. The speech recognition engine should be independent of commercial products and services (where the dictionary could not be modified. Using of third-party services could be also a security and privacy problem in specific applications, when the unsecured audio data could not be sent to uncontrolled environments (voice data transferred to servers around the globe. Using our experience with speech recognition applications, we have been able to construct a multi-thread speech recognition serverbased solution designed for simple applications interface (API to speech recognition engine modified to specific needs of particular application.

What Types of Visual Recognition Tasks Are Mediated by the Neural Subsystem that Subserves Face Recognition?

Science.gov (United States)

Brooks, Brian E.; Cooper, Eric E.

2006-01-01

Three divided visual field experiments tested current hypotheses about the types of visual shape representation tasks that recruit the cognitive and neural mechanisms underlying face recognition. Experiment 1 found a right hemisphere advantage for subordinate but not basic-level face recognition. Experiment 2 found a right hemisphere advantage for…

Effects of modality and repetition in a continuous recognition memory task: Repetition has no effect on auditory recognition memory.

Science.gov (United States)

Amir Kassim, Azlina; Rehman, Rehan; Price, Jessica M

2018-04-01

Previous research has shown that auditory recognition memory is poorer compared to visual and cross-modal (visual and auditory) recognition memory. The effect of repetition on memory has been robust in showing improved performance. It is not clear, however, how auditory recognition memory compares to visual and cross-modal recognition memory following repetition. Participants performed a recognition memory task, making old/new discriminations to new stimuli, stimuli repeated for the first time after 4-7 intervening items (R1), or repeated for the second time after 36-39 intervening items (R2). Depending on the condition, participants were either exposed to visual stimuli (2D line drawings), auditory stimuli (spoken words), or cross-modal stimuli (pairs of images and associated spoken words). Results showed that unlike participants in the visual and cross-modal conditions, participants in the auditory recognition did not show improvements in performance on R2 trials compared to R1 trials. These findings have implications for pedagogical techniques in education, as well as for interventions and exercises aimed at boosting memory performance. Copyright © 2018 Elsevier B.V. All rights reserved.

Novel acoustic features for speech emotion recognition

Institute of Scientific and Technical Information of China (English)

ROH; Yong-Wan; KIM; Dong-Ju; LEE; Woo-Seok; HONG; Kwang-Seok

2009-01-01

This paper focuses on acoustic features that effectively improve the recognition of emotion in human speech.The novel features in this paper are based on spectral-based entropy parameters such as fast Fourier transform(FFT) spectral entropy,delta FFT spectral entropy,Mel-frequency filter bank(MFB) spectral entropy,and Delta MFB spectral entropy.Spectral-based entropy features are simple.They reflect frequency characteristic and changing characteristic in frequency of speech.We implement an emotion rejection module using the probability distribution of recognized-scores and rejected-scores.This reduces the false recognition rate to improve overall performance.Recognized-scores and rejected-scores refer to probabilities of recognized and rejected emotion recognition results,respectively.These scores are first obtained from a pattern recognition procedure.The pattern recognition phase uses the Gaussian mixture model(GMM).We classify the four emotional states as anger,sadness,happiness and neutrality.The proposed method is evaluated using 45 sentences in each emotion for 30 subjects,15 males and 15 females.Experimental results show that the proposed method is superior to the existing emotion recognition methods based on GMM using energy,Zero Crossing Rate(ZCR),linear prediction coefficient(LPC),and pitch parameters.We demonstrate the effectiveness of the proposed approach.One of the proposed features,combined MFB and delta MFB spectral entropy improves performance approximately 10% compared to the existing feature parameters for speech emotion recognition methods.We demonstrate a 4% performance improvement in the applied emotion rejection with low confidence score.

Novel acoustic features for speech emotion recognition

Institute of Scientific and Technical Information of China (English)

ROH Yong-Wan; KIM Dong-Ju; LEE Woo-Seok; HONG Kwang-Seok

2009-01-01

This paper focuses on acoustic features that effectively improve the recognition of emotion in human speech. The novel features in this paper are based on spectral-based entropy parameters such as fast Fourier transform (FFT) spectral entropy, delta FFT spectral entropy, Mel-frequency filter bank (MFB)spectral entropy, and Delta MFB spectral entropy. Spectral-based entropy features are simple. They reflect frequency characteristic and changing characteristic in frequency of speech. We implement an emotion rejection module using the probability distribution of recognized-scores and rejected-scores.This reduces the false recognition rate to improve overall performance. Recognized-scores and rejected-scores refer to probabilities of recognized and rejected emotion recognition results, respectively.These scores are first obtained from a pattern recognition procedure. The pattern recognition phase uses the Gaussian mixture model (GMM). We classify the four emotional states as anger, sadness,happiness and neutrality. The proposed method is evaluated using 45 sentences in each emotion for 30 subjects, 15 males and 15 females. Experimental results show that the proposed method is superior to the existing emotion recognition methods based on GMM using energy, Zero Crossing Rate (ZCR),linear prediction coefficient (LPC), and pitch parameters. We demonstrate the effectiveness of the proposed approach. One of the proposed features, combined MFB and delta MFB spectral entropy improves performance approximately 10% compared to the existing feature parameters for speech emotion recognition methods. We demonstrate a 4% performance improvement in the applied emotion rejection with low confidence score.

Three-dimensional fingerprint recognition by using convolution neural network

Science.gov (United States)

Tian, Qianyu; Gao, Nan; Zhang, Zonghua

2018-01-01

With the development of science and technology and the improvement of social information, fingerprint recognition technology has become a hot research direction and been widely applied in many actual fields because of its feasibility and reliability. The traditional two-dimensional (2D) fingerprint recognition method relies on matching feature points. This method is not only time-consuming, but also lost three-dimensional (3D) information of fingerprint, with the fingerprint rotation, scaling, damage and other issues, a serious decline in robustness. To solve these problems, 3D fingerprint has been used to recognize human being. Because it is a new research field, there are still lots of challenging problems in 3D fingerprint recognition. This paper presents a new 3D fingerprint recognition method by using a convolution neural network (CNN). By combining 2D fingerprint and fingerprint depth map into CNN, and then through another CNN feature fusion, the characteristics of the fusion complete 3D fingerprint recognition after classification. This method not only can preserve 3D information of fingerprints, but also solves the problem of CNN input. Moreover, the recognition process is simpler than traditional feature point matching algorithm. 3D fingerprint recognition rate by using CNN is compared with other fingerprint recognition algorithms. The experimental results show that the proposed 3D fingerprint recognition method has good recognition rate and robustness.

A New Database for Speaker Recognition

DEFF Research Database (Denmark)

Feng, Ling; Hansen, Lars Kai

2005-01-01

In this paper we discuss properties of speech databases used for speaker recognition research and evaluation, and we characterize some popular standard databases. The paper presents a new database called ELSDSR dedicated to speaker recognition applications. The main characteristics of this database...

Recognition without Identification for Words, Pseudowords and Nonwords

Science.gov (United States)

Arndt, Jason; Lee, Karen; Flora, David B.

2008-01-01

Three experiments examined whether the representations underlying recognition memory familiarity can be episodic in nature. Recognition without identification [Cleary, A. M., & Greene, R. L. (2000). Recognition without identification. "Journal of Experimental Psychology: Learning, Memory, and Cognition," 26, 1063-1069; Peynircioglu, Z. F. (1990).…

Hybrid gesture recognition system for short-range use

Science.gov (United States)

Minagawa, Akihiro; Fan, Wei; Katsuyama, Yutaka; Takebe, Hiroaki; Ozawa, Noriaki; Hotta, Yoshinobu; Sun, Jun

2012-03-01

In recent years, various gesture recognition systems have been studied for use in television and video games[1]. In such systems, motion areas ranging from 1 to 3 meters deep have been evaluated[2]. However, with the burgeoning popularity of small mobile displays, gesture recognition systems capable of operating at much shorter ranges have become necessary. The problems related to such systems are exacerbated by the fact that the camera's field of view is unknown to the user during operation, which imposes several restrictions on his/her actions. To overcome the restrictions generated from such mobile camera devices, and to create a more flexible gesture recognition interface, we propose a hybrid hand gesture system, in which two types of gesture recognition modules are prepared and with which the most appropriate recognition module is selected by a dedicated switching module. The two recognition modules of this system are shape analysis using a boosting approach (detection-based approach)[3] and motion analysis using image frame differences (motion-based approach)(for example, see[4]). We evaluated this system using sample users and classified the resulting errors into three categories: errors that depend on the recognition module, errors caused by incorrect module identification, and errors resulting from user actions. In this paper, we show the results of our investigations and explain the problems related to short-range gesture recognition systems.

Automatic modulation recognition of communication signals

CERN Document Server

Azzouz, Elsayed Elsayed

1996-01-01

Automatic modulation recognition is a rapidly evolving area of signal analysis. In recent years, interest from the academic and military research institutes has focused around the research and development of modulation recognition algorithms. Any communication intelligence (COMINT) system comprises three main blocks: receiver front-end, modulation recogniser and output stage. Considerable work has been done in the area of receiver front-ends. The work at the output stage is concerned with information extraction, recording and exploitation and begins with signal demodulation, that requires accurate knowledge about the signal modulation type. There are, however, two main reasons for knowing the current modulation type of a signal; to preserve the signal information content and to decide upon the suitable counter action, such as jamming. Automatic Modulation Recognition of Communications Signals describes in depth this modulation recognition process. Drawing on several years of research, the authors provide a cr...

Image quality assessment for video stream recognition systems

Science.gov (United States)

Chernov, Timofey S.; Razumnuy, Nikita P.; Kozharinov, Alexander S.; Nikolaev, Dmitry P.; Arlazarov, Vladimir V.

2018-04-01

Recognition and machine vision systems have long been widely used in many disciplines to automate various processes of life and industry. Input images of optical recognition systems can be subjected to a large number of different distortions, especially in uncontrolled or natural shooting conditions, which leads to unpredictable results of recognition systems, making it impossible to assess their reliability. For this reason, it is necessary to perform quality control of the input data of recognition systems, which is facilitated by modern progress in the field of image quality evaluation. In this paper, we investigate the approach to designing optical recognition systems with built-in input image quality estimation modules and feedback, for which the necessary definitions are introduced and a model for describing such systems is constructed. The efficiency of this approach is illustrated by the example of solving the problem of selecting the best frames for recognition in a video stream for a system with limited resources. Experimental results are presented for the system for identity documents recognition, showing a significant increase in the accuracy and speed of the system under simulated conditions of automatic camera focusing, leading to blurring of frames.

Artificial Neural Network Based Optical Character Recognition

OpenAIRE

Vivek Shrivastava; Navdeep Sharma

2012-01-01

Optical Character Recognition deals in recognition and classification of characters from an image. For the recognition to be accurate, certain topological and geometrical properties are calculated, based on which a character is classified and recognized. Also, the Human psychology perceives characters by its overall shape and features such as strokes, curves, protrusions, enclosures etc. These properties, also called Features are extracted from the image by means of spatial pixel-...

Mathematical symbol hypothesis recognition with rejection option

OpenAIRE

Julca-Aguilar , Frank; Hirata , Nina ,; Viard-Gaudin , Christian; Mouchère , Harold; Medjkoune , Sofiane

2014-01-01

International audience; In the context of handwritten mathematical expressions recognition, a first step consist on grouping strokes (segmentation) to form symbol hypotheses: groups of strokes that might represent a symbol. Then, the symbol recognition step needs to cope with the identification of wrong segmented symbols (false hypotheses). However, previous works on symbol recognition consider only correctly segmented symbols. In this work, we focus on the problem of mathematical symbol reco...

Interest and attention in facial recognition.

Science.gov (United States)

Burgess, Melinda C R; Weaver, George E

2003-04-01

When applied to facial recognition, the levels of processing paradigm has yielded consistent results: faces processed in deep conditions are recognized better than faces processed under shallow conditions. However, there are multiple explanations for this occurrence. The own-race advantage in facial recognition, the tendency to recognize faces from one's own race better than faces from another race, is also consistently shown but not clearly explained. This study was designed to test the hypothesis that the levels of processing findings in facial recognition are a result of interest and attention, not differences in processing. This hypothesis was tested for both own and other faces with 105 Caucasian general psychology students. Levels of processing was manipulated as a between-subjects variable; students were asked to answer one of four types of study questions, e.g., "deep" or "shallow" processing questions, while viewing the study faces. Students' recognition of a subset of previously presented Caucasian and African-American faces from a test-set with an equal number of distractor faces was tested. They indicated their interest in and attention to the task. The typical levels of processing effect was observed with better recognition performance in the deep conditions than in the shallow conditions for both own- and other-race faces. The typical own-race advantage was also observed regardless of level of processing condition. For both own- and other-race faces, level of processing explained a significant portion of the recognition variance above and beyond what was explained by interest in and attention to the task.

Obligatory and facultative brain regions for voice-identity recognition

Science.gov (United States)

Roswandowitz, Claudia; Kappes, Claudia; Obrig, Hellmuth; von Kriegstein, Katharina

2018-01-01

Abstract Recognizing the identity of others by their voice is an important skill for social interactions. To date, it remains controversial which parts of the brain are critical structures for this skill. Based on neuroimaging findings, standard models of person-identity recognition suggest that the right temporal lobe is the hub for voice-identity recognition. Neuropsychological case studies, however, reported selective deficits of voice-identity recognition in patients predominantly with right inferior parietal lobe lesions. Here, our aim was to work towards resolving the discrepancy between neuroimaging studies and neuropsychological case studies to find out which brain structures are critical for voice-identity recognition in humans. We performed a voxel-based lesion-behaviour mapping study in a cohort of patients (n = 58) with unilateral focal brain lesions. The study included a comprehensive behavioural test battery on voice-identity recognition of newly learned (voice-name, voice-face association learning) and familiar voices (famous voice recognition) as well as visual (face-identity recognition) and acoustic control tests (vocal-pitch and vocal-timbre discrimination). The study also comprised clinically established tests (neuropsychological assessment, audiometry) and high-resolution structural brain images. The three key findings were: (i) a strong association between voice-identity recognition performance and right posterior/mid temporal and right inferior parietal lobe lesions; (ii) a selective association between right posterior/mid temporal lobe lesions and voice-identity recognition performance when face-identity recognition performance was factored out; and (iii) an association of right inferior parietal lobe lesions with tasks requiring the association between voices and faces but not voices and names. The results imply that the right posterior/mid temporal lobe is an obligatory structure for voice-identity recognition, while the inferior parietal

Object recognition memory in zebrafish.

Science.gov (United States)

May, Zacnicte; Morrill, Adam; Holcombe, Adam; Johnston, Travis; Gallup, Joshua; Fouad, Karim; Schalomon, Melike; Hamilton, Trevor James

2016-01-01

The novel object recognition, or novel-object preference (NOP) test is employed to assess recognition memory in a variety of organisms. The subject is exposed to two identical objects, then after a delay, it is placed back in the original environment containing one of the original objects and a novel object. If the subject spends more time exploring one object, this can be interpreted as memory retention. To date, this test has not been fully explored in zebrafish (Danio rerio). Zebrafish possess recognition memory for simple 2- and 3-dimensional geometrical shapes, yet it is unknown if this translates to complex 3-dimensional objects. In this study we evaluated recognition memory in zebrafish using complex objects of different sizes. Contrary to rodents, zebrafish preferentially explored familiar over novel objects. Familiarity preference disappeared after delays of 5 mins. Leopard danios, another strain of D. rerio, also preferred the familiar object after a 1 min delay. Object preference could be re-established in zebra danios by administration of nicotine tartrate salt (50mg/L) prior to stimuli presentation, suggesting a memory-enhancing effect of nicotine. Additionally, exploration biases were present only when the objects were of intermediate size (2 × 5 cm). Our results demonstrate zebra and leopard danios have recognition memory, and that low nicotine doses can improve this memory type in zebra danios. However, exploration biases, from which memory is inferred, depend on object size. These findings suggest zebrafish ecology might influence object preference, as zebrafish neophobia could reflect natural anti-predatory behaviour. Copyright © 2015 Elsevier B.V. All rights reserved.

Speaker Recognition

DEFF Research Database (Denmark)

Mølgaard, Lasse Lohilahti; Jørgensen, Kasper Winther

2005-01-01

Speaker recognition is basically divided into speaker identification and speaker verification. Verification is the task of automatically determining if a person really is the person he or she claims to be. This technology can be used as a biometric feature for verifying the identity of a person...

ACOUSTIC SPEECH RECOGNITION FOR MARATHI LANGUAGE USING SPHINX

Directory of Open Access Journals (Sweden)

Aman Ankit

2016-09-01

Full Text Available Speech recognition or speech to text processing, is a process of recognizing human speech by the computer and converting into text. In speech recognition, transcripts are created by taking recordings of speech as audio and their text transcriptions. Speech based applications which include Natural Language Processing (NLP techniques are popular and an active area of research. Input to such applications is in natural language and output is obtained in natural language. Speech recognition mostly revolves around three approaches namely Acoustic phonetic approach, Pattern recognition approach and Artificial intelligence approach. Creation of acoustic model requires a large database of speech and training algorithms. The output of an ASR system is recognition and translation of spoken language into text by computers and computerized devices. ASR today finds enormous application in tasks that require human machine interfaces like, voice dialing, and etc. Our key contribution in this paper is to create corpora for Marathi language and explore the use of Sphinx engine for automatic speech recognition

A Review on Video-Based Human Activity Recognition

Directory of Open Access Journals (Sweden)

Shian-Ru Ke

2013-06-01

Full Text Available This review article surveys extensively the current progresses made toward video-based human activity recognition. Three aspects for human activity recognition are addressed including core technology, human activity recognition systems, and applications from low-level to high-level representation. In the core technology, three critical processing stages are thoroughly discussed mainly: human object segmentation, feature extraction and representation, activity detection and classification algorithms. In the human activity recognition systems, three main types are mentioned, including single person activity recognition, multiple people interaction and crowd behavior, and abnormal activity recognition. Finally the domains of applications are discussed in detail, specifically, on surveillance environments, entertainment environments and healthcare systems. Our survey, which aims to provide a comprehensive state-of-the-art review of the field, also addresses several challenges associated with these systems and applications. Moreover, in this survey, various applications are discussed in great detail, specifically, a survey on the applications in healthcare monitoring systems.

Does Employee Recognition Affect Positive Psychological Functioning and Well-Being?

Science.gov (United States)

Merino, M Dolores; Privado, Jesús

2015-09-14

Employee recognition is one of the typical characteristics of healthy organizations. The majority of research on recognition has studied the consequences of this variable on workers. But few investigations have focused on understanding what mechanisms mediate between recognition and its consequences. This work aims to understand whether the relationship between employee recognition and well-being, psychological resources mediate. To answer this question a sample of 1831 workers was used. The variables measured were: employee recognition, subjective well-being and positive psychological functioning (PPF), which consists of 11 psychological resources. In the analysis of data, structural equation models were applied. The results confirmed our hypothesis and showed that PPF mediate the relationship between recognition and well-being. The effect of recognition over PPF is two times greater (.39) with peer-recognition than with supervisor-recognition (.20), and, the effect of PPF over well-being is .59. This study highlights the importance of promoting employee recognition policies in organizations for the impact it has, not only on well-being, but also on the positive psychological functioning of the workers.

Robust speaker recognition in noisy environments

CERN Document Server

Rao, K Sreenivasa

2014-01-01

This book discusses speaker recognition methods to deal with realistic variable noisy environments. The text covers authentication systems for; robust noisy background environments, functions in real time and incorporated in mobile devices. The book focuses on different approaches to enhance the accuracy of speaker recognition in presence of varying background environments. The authors examine: (a) Feature compensation using multiple background models, (b) Feature mapping using data-driven stochastic models, (c) Design of super vector- based GMM-SVM framework for robust speaker recognition, (d) Total variability modeling (i-vectors) in a discriminative framework and (e) Boosting method to fuse evidences from multiple SVM models.

Pattern activation/recognition theory of mind.

Science.gov (United States)

du Castel, Bertrand

2015-01-01

In his 2012 book How to Create a Mind, Ray Kurzweil defines a "Pattern Recognition Theory of Mind" that states that the brain uses millions of pattern recognizers, plus modules to check, organize, and augment them. In this article, I further the theory to go beyond pattern recognition and include also pattern activation, thus encompassing both sensory and motor functions. In addition, I treat checking, organizing, and augmentation as patterns of patterns instead of separate modules, therefore handling them the same as patterns in general. Henceforth I put forward a unified theory I call "Pattern Activation/Recognition Theory of Mind." While the original theory was based on hierarchical hidden Markov models, this evolution is based on their precursor: stochastic grammars. I demonstrate that a class of self-describing stochastic grammars allows for unifying pattern activation, recognition, organization, consistency checking, metaphor, and learning, into a single theory that expresses patterns throughout. I have implemented the model as a probabilistic programming language specialized in activation/recognition grammatical and neural operations. I use this prototype to compute and present diagrams for each stochastic grammar and corresponding neural circuit. I then discuss the theory as it relates to artificial network developments, common coding, neural reuse, and unity of mind, concluding by proposing potential paths to validation.

Fast neuromimetic object recognition using FPGA outperforms GPU implementations.

Science.gov (United States)

Orchard, Garrick; Martin, Jacob G; Vogelstein, R Jacob; Etienne-Cummings, Ralph

2013-08-01

Recognition of objects in still images has traditionally been regarded as a difficult computational problem. Although modern automated methods for visual object recognition have achieved steadily increasing recognition accuracy, even the most advanced computational vision approaches are unable to obtain performance equal to that of humans. This has led to the creation of many biologically inspired models of visual object recognition, among them the hierarchical model and X (HMAX) model. HMAX is traditionally known to achieve high accuracy in visual object recognition tasks at the expense of significant computational complexity. Increasing complexity, in turn, increases computation time, reducing the number of images that can be processed per unit time. In this paper we describe how the computationally intensive and biologically inspired HMAX model for visual object recognition can be modified for implementation on a commercial field-programmable aate Array, specifically the Xilinx Virtex 6 ML605 evaluation board with XC6VLX240T FPGA. We show that with minor modifications to the traditional HMAX model we can perform recognition on images of size 128 × 128 pixels at a rate of 190 images per second with a less than 1% loss in recognition accuracy in both binary and multiclass visual object recognition tasks.

Graded Mirror Self-Recognition by Clark's Nutcrackers.

Science.gov (United States)

Clary, Dawson; Kelly, Debbie M

2016-11-04

The traditional 'mark test' has shown some large-brained species are capable of mirror self-recognition. During this test a mark is inconspicuously placed on an animal's body where it can only be seen with the aid of a mirror. If the animal increases the number of actions directed to the mark region when presented with a mirror, the animal is presumed to have recognized the mirror image as its reflection. However, the pass/fail nature of the mark test presupposes self-recognition exists in entirety or not at all. We developed a novel mirror-recognition task, to supplement the mark test, which revealed gradation in the self-recognition of Clark's nutcrackers, a large-brained corvid. To do so, nutcrackers cached food alone, observed by another nutcracker, or with a regular or blurry mirror. The nutcrackers suppressed caching with a regular mirror, a behavioural response to prevent cache theft by conspecifics, but did not suppress caching with a blurry mirror. Likewise, during the mark test, most nutcrackers made more self-directed actions to the mark with a blurry mirror than a regular mirror. Both results suggest self-recognition was more readily achieved with the blurry mirror and that self-recognition may be more broadly present among animals than currently thought.

Hierarchical Context Modeling for Video Event Recognition.

Science.gov (United States)

Wang, Xiaoyang; Ji, Qiang

2016-10-11

Current video event recognition research remains largely target-centered. For real-world surveillance videos, targetcentered event recognition faces great challenges due to large intra-class target variation, limited image resolution, and poor detection and tracking results. To mitigate these challenges, we introduced a context-augmented video event recognition approach. Specifically, we explicitly capture different types of contexts from three levels including image level, semantic level, and prior level. At the image level, we introduce two types of contextual features including the appearance context features and interaction context features to capture the appearance of context objects and their interactions with the target objects. At the semantic level, we propose a deep model based on deep Boltzmann machine to learn event object representations and their interactions. At the prior level, we utilize two types of prior-level contexts including scene priming and dynamic cueing. Finally, we introduce a hierarchical context model that systematically integrates the contextual information at different levels. Through the hierarchical context model, contexts at different levels jointly contribute to the event recognition. We evaluate the hierarchical context model for event recognition on benchmark surveillance video datasets. Results show that incorporating contexts in each level can improve event recognition performance, and jointly integrating three levels of contexts through our hierarchical model achieves the best performance.

Oxytocin improves emotion recognition for older males.

Science.gov (United States)

Campbell, Anna; Ruffman, Ted; Murray, Janice E; Glue, Paul

2014-10-01

Older adults (≥60 years) perform worse than young adults (18-30 years) when recognizing facial expressions of emotion. The hypothesized cause of these changes might be declines in neurotransmitters that could affect information processing within the brain. In the present study, we examined the neuropeptide oxytocin that functions to increase neurotransmission. Research suggests that oxytocin benefits the emotion recognition of less socially able individuals. Men tend to have lower levels of oxytocin and older men tend to have worse emotion recognition than older women; therefore, there is reason to think that older men will be particularly likely to benefit from oxytocin. We examined this idea using a double-blind design, testing 68 older and 68 young adults randomly allocated to receive oxytocin nasal spray (20 international units) or placebo. Forty-five minutes afterward they completed an emotion recognition task assessing labeling accuracy for angry, disgusted, fearful, happy, neutral, and sad faces. Older males receiving oxytocin showed improved emotion recognition relative to those taking placebo. No differences were found for older females or young adults. We hypothesize that oxytocin facilitates emotion recognition by improving neurotransmission in the group with the worst emotion recognition. Copyright © 2014 Elsevier Inc. All rights reserved.

Discrete classification technique applied to TV advertisements liking recognition system based on low-cost EEG headsets.

Science.gov (United States)

Soria Morillo, Luis M; Alvarez-Garcia, Juan A; Gonzalez-Abril, Luis; Ortega Ramírez, Juan A

2016-07-15

In this paper a new approach is applied to the area of marketing research. The aim of this paper is to recognize how brain activity responds during the visualization of short video advertisements using discrete classification techniques. By means of low cost electroencephalography devices (EEG), the activation level of some brain regions have been studied while the ads are shown to users. We may wonder about how useful is the use of neuroscience knowledge in marketing, or what could provide neuroscience to marketing sector, or why this approach can improve the accuracy and the final user acceptance compared to other works. By using discrete techniques over EEG frequency bands of a generated dataset, C4.5, ANN and the new recognition system based on Ameva, a discretization algorithm, is applied to obtain the score given by subjects to each TV ad. The proposed technique allows to reach more than 75 % of accuracy, which is an excellent result taking into account the typology of EEG sensors used in this work. Furthermore, the time consumption of the algorithm proposed is reduced up to 30 % compared to other techniques presented in this paper. This bring about a battery lifetime improvement on the devices where the algorithm is running, extending the experience in the ubiquitous context where the new approach has been tested.

The Role of Perceptual Load in Object Recognition

Science.gov (United States)

Lavie, Nilli; Lin, Zhicheng; Zokaei, Nahid; Thoma, Volker

2009-01-01

Predictions from perceptual load theory (Lavie, 1995, 2005) regarding object recognition across the same or different viewpoints were tested. Results showed that high perceptual load reduces distracter recognition levels despite always presenting distracter objects from the same view. They also showed that the levels of distracter recognition were…

Weighted Feature Gaussian Kernel SVM for Emotion Recognition.

Science.gov (United States)

Wei, Wei; Jia, Qingxuan

2016-01-01

Emotion recognition with weighted feature based on facial expression is a challenging research topic and has attracted great attention in the past few years. This paper presents a novel method, utilizing subregion recognition rate to weight kernel function. First, we divide the facial expression image into some uniform subregions and calculate corresponding recognition rate and weight. Then, we get a weighted feature Gaussian kernel function and construct a classifier based on Support Vector Machine (SVM). At last, the experimental results suggest that the approach based on weighted feature Gaussian kernel function has good performance on the correct rate in emotion recognition. The experiments on the extended Cohn-Kanade (CK+) dataset show that our method has achieved encouraging recognition results compared to the state-of-the-art methods.

Employee Recognition and Performance: A Field Experiment

OpenAIRE

Bradler, Christiane; Dur, Robert; Neckermann, Susanne; Non, Arjan

2014-01-01

This discussion paper led to a publication in 'Management Science' . This paper reports the results from a controlled field experiment designed to investigate the causal effect of unannounced, public recognition on employee performance. We hired more than 300 employees to work on a three-hour data-entry task. In a random sample of work groups, workers unexpectedly received recognition after two hours of work. We find that recognition increases subsequent performance substantially, and particu...

Context reinstatement in recognition: memory and beyond

OpenAIRE

Hanczakowski, M; Zawadzka, K; Coote, L

2014-01-01

Context effects in recognition tests are twofold. First, presenting familiar contexts at a test leads to an attribution of context familiarity to a recognition probe, which has been dubbed ‘context-dependent recognition’. Second, reinstating the exact study context for a particular target in a recognition test cues recollection of an item-context association, resulting in ‘context-dependent discrimination’. Here we investigated how these two context effects are expressed in metacognitive moni...

Color descriptors for object category recognition

NARCIS (Netherlands)

van de Sande, K.E.A.; Gevers, T.; Snoek, C.G.M.

2008-01-01

Category recognition is important to access visual information on the level of objects. A common approach is to compute image descriptors first and then to apply machine learning to achieve category recognition from annotated examples. As a consequence, the choice of image descriptors is of great

New technique for number-plate recognition

Science.gov (United States)

Guo, Jie; Shi, Peng-Fei

2001-09-01

This paper presents an alternative algorithm for number plate recognition. The algorithm consists of three modules. Respectively, they are number plate location module, character segmentation module and character recognition module. Number plate location module extracts the number plate from the detected car image by analyzing the color and the texture properties. Different from most license plate location methods, the algorithm has fewer limits to the car size, the car position in the image and the image background. Character segmentation module applies connected region algorithm both to eliminate noise points and to segment characters. Touching characters and broken characters can be processed correctly. Character recognition module recognizes characters with HHIC (Hierarchical Hybrid Integrated Classifier). The system has been tested with 100 images obtained from crossroad and parking lot, etc, where the cars have different size, position, background and illumination. Successful recognition rate is about 92%. The average processing time is 1.2 second.

Modulation of pathogen recognition by autophagy

Directory of Open Access Journals (Sweden)

Ji Eun eOh

2012-03-01

Full Text Available Autophagy is an ancient biological process for maintaining cellular homeostasis by degradation of long-lived cytosolic proteins and organelles. Recent studies demonstrated that autophagy is availed by immune cells to regulate innate immunity. On the one hand, cells exert direct effector function by degrading intracellular pathogens; on the other hand, autophagy modulates pathogen recognition and downstream signaling for innate immune responses. Pathogen recognition via pattern recognition receptors induces autophagy. The function of phagocytic cells is enhanced by recruitment of autophagy-related proteins. Moreover, autophagy acts as a delivery system for viral replication complexes to migrate to the endosomal compartments where virus sensing occurs. In another case, key molecules of the autophagic pathway have been found to negatively regulate immune signaling, thus preventing aberrant activation of cytokine production and consequent immune responses. In this review, we focus on the recent advances in the role of autophagy in pathogen recognition and modulation of innate immune responses.

Emotional valence of stimuli modulates false recognition: Using a modified version of the simplified conjoint recognition paradigm.

Science.gov (United States)

Gong, Xianmin; Xiao, Hongrui; Wang, Dahua

2016-11-01

False recognition results from the interplay of multiple cognitive processes, including verbatim memory, gist memory, phantom recollection, and response bias. In the current study, we modified the simplified Conjoint Recognition (CR) paradigm to investigate the way in which the valence of emotional stimuli affects the cognitive process and behavioral outcome of false recognition. In Study 1, we examined the applicability of the modification to the simplified CR paradigm and model. Twenty-six undergraduate students (13 females, aged 21.00±2.30years) learned and recognized both the large and small categories of photo objects. The applicability of the paradigm and model was confirmed by a fair goodness-of-fit of the model to the observational data and by their competence in detecting the memory differences between the large- and small-category conditions. In Study 2, we recruited another sample of 29 undergraduate students (14 females, aged 22.60±2.74years) to learn and recognize the categories of photo objects that were emotionally provocative. The results showed that negative valence increased false recognition, particularly the rate of false "remember" responses, by facilitating phantom recollection; positive valence did not influence false recognition significantly though enhanced gist processing. Copyright © 2016 Elsevier B.V. All rights reserved.

Neural-Network Object-Recognition Program

Science.gov (United States)

Spirkovska, L.; Reid, M. B.

1993-01-01

HONTIOR computer program implements third-order neural network exhibiting invariance under translation, change of scale, and in-plane rotation. Invariance incorporated directly into architecture of network. Only one view of each object needed to train network for two-dimensional-translation-invariant recognition of object. Also used for three-dimensional-transformation-invariant recognition by training network on only set of out-of-plane rotated views. Written in C language.

SPEECH EMOTION RECOGNITION USING MODIFIED QUADRATIC DISCRIMINATION FUNCTION

Institute of Scientific and Technical Information of China (English)

无

2008-01-01

Quadratic Discrimination Function(QDF)is commonly used in speech emotion recognition,which proceeds on the premise that the input data is normal distribution.In this Paper,we propose a transformation to normalize the emotional features,then derivate a Modified QDF(MQDF) to speech emotion recognition.Features based on prosody and voice quality are extracted and Principal Component Analysis Neural Network (PCANN) is used to reduce dimension of the feature vectors.The results show that voice quality features are effective supplement for recognition.and the method in this paper could improve the recognition ratio effectively.

Two processes support visual recognition memory in rhesus monkeys.

Science.gov (United States)

Guderian, Sebastian; Brigham, Danielle; Mishkin, Mortimer

2011-11-29

A large body of evidence in humans suggests that recognition memory can be supported by both recollection and familiarity. Recollection-based recognition is characterized by the retrieval of contextual information about the episode in which an item was previously encountered, whereas familiarity-based recognition is characterized instead by knowledge only that the item had been encountered previously in the absence of any context. To date, it is unknown whether monkeys rely on similar mnemonic processes to perform recognition memory tasks. Here, we present evidence from the analysis of receiver operating characteristics, suggesting that visual recognition memory in rhesus monkeys also can be supported by two separate processes and that these processes have features considered to be characteristic of recollection and familiarity. Thus, the present study provides converging evidence across species for a dual process model of recognition memory and opens up the possibility of studying the neural mechanisms of recognition memory in nonhuman primates on tasks that are highly similar to the ones used in humans.

Impaired recognition of happy facial expressions in bipolar disorder.

Science.gov (United States)

Lawlor-Savage, Linette; Sponheim, Scott R; Goghari, Vina M

2014-08-01

The ability to accurately judge facial expressions is important in social interactions. Individuals with bipolar disorder have been found to be impaired in emotion recognition; however, the specifics of the impairment are unclear. This study investigated whether facial emotion recognition difficulties in bipolar disorder reflect general cognitive, or emotion-specific, impairments. Impairment in the recognition of particular emotions and the role of processing speed in facial emotion recognition were also investigated. Clinically stable bipolar patients (n = 17) and healthy controls (n = 50) judged five facial expressions in two presentation types, time-limited and self-paced. An age recognition condition was used as an experimental control. Bipolar patients' overall facial recognition ability was unimpaired. However, patients' specific ability to judge happy expressions under time constraints was impaired. Findings suggest a deficit in happy emotion recognition impacted by processing speed. Given the limited sample size, further investigation with a larger patient sample is warranted.

Face and body recognition show similar improvement during childhood.

Science.gov (United States)

Bank, Samantha; Rhodes, Gillian; Read, Ainsley; Jeffery, Linda

2015-09-01

Adults are proficient in extracting identity cues from faces. This proficiency develops slowly during childhood, with performance not reaching adult levels until adolescence. Bodies are similar to faces in that they convey identity cues and rely on specialized perceptual mechanisms. However, it is currently unclear whether body recognition mirrors the slow development of face recognition during childhood. Recent evidence suggests that body recognition develops faster than face recognition. Here we measured body and face recognition in 6- and 10-year-old children and adults to determine whether these two skills show different amounts of improvement during childhood. We found no evidence that they do. Face and body recognition showed similar improvement with age, and children, like adults, were better at recognizing faces than bodies. These results suggest that the mechanisms of face and body memory mature at a similar rate or that improvement of more general cognitive and perceptual skills underlies improvement of both face and body recognition. Copyright © 2015 Elsevier Inc. All rights reserved.

Human face recognition ability is specific and highly heritable.

Science.gov (United States)

Wilmer, Jeremy B; Germine, Laura; Chabris, Christopher F; Chatterjee, Garga; Williams, Mark; Loken, Eric; Nakayama, Ken; Duchaine, Bradley

2010-03-16

Compared with notable successes in the genetics of basic sensory transduction, progress on the genetics of higher level perception and cognition has been limited. We propose that investigating specific cognitive abilities with well-defined neural substrates, such as face recognition, may yield additional insights. In a twin study of face recognition, we found that the correlation of scores between monozygotic twins (0.70) was more than double the dizygotic twin correlation (0.29), evidence for a high genetic contribution to face recognition ability. Low correlations between face recognition scores and visual and verbal recognition scores indicate that both face recognition ability itself and its genetic basis are largely attributable to face-specific mechanisms. The present results therefore identify an unusual phenomenon: a highly specific cognitive ability that is highly heritable. Our results establish a clear genetic basis for face recognition, opening this intensively studied and socially advantageous cognitive trait to genetic investigation.

Effects of Instructions on False Recognition.

Science.gov (United States)

Mueller, John H.; And Others

Four experiments were conducted to examine the effects of various processing instructions on the rate of false recognition. The continuous single-item procedure was used, and false recognitions of four types were examined: synonyms, antonyms, nonsemantic associates, and homonyms. The instructions encouraged subjects to think of associates, usages…

Iris recognition in the presence of ocular disease.

Science.gov (United States)

Aslam, Tariq Mehmood; Tan, Shi Zhuan; Dhillon, Baljean

2009-05-06

Iris recognition systems are among the most accurate of all biometric technologies with immense potential for use in worldwide security applications. This study examined the effect of eye pathology on iris recognition and in particular whether eye disease could cause iris recognition systems to fail. The experiment involved a prospective cohort of 54 patients with anterior segment eye disease who were seen at the acute referral unit of the Princess Alexandra Eye Pavilion in Edinburgh. Iris camera images were obtained from patients before treatment was commenced and again at follow-up appointments after treatment had been given. The principal outcome measure was that of mathematical difference in the iris recognition templates obtained from patients' eyes before and after treatment of the eye disease. Results showed that the performance of iris recognition was remarkably resilient to most ophthalmic disease states, including corneal oedema, iridotomies (laser puncture of iris) and conjunctivitis. Problems were, however, encountered in some patients with acute inflammation of the iris (iritis/anterior uveitis). The effects of a subject developing anterior uveitis may cause current recognition systems to fail. Those developing and deploying iris recognition should be aware of the potential problems that this could cause to this key biometric technology.

Support vector machine for automatic pain recognition

Science.gov (United States)

Monwar, Md Maruf; Rezaei, Siamak

2009-02-01

Facial expressions are a key index of emotion and the interpretation of such expressions of emotion is critical to everyday social functioning. In this paper, we present an efficient video analysis technique for recognition of a specific expression, pain, from human faces. We employ an automatic face detector which detects face from the stored video frame using skin color modeling technique. For pain recognition, location and shape features of the detected faces are computed. These features are then used as inputs to a support vector machine (SVM) for classification. We compare the results with neural network based and eigenimage based automatic pain recognition systems. The experiment results indicate that using support vector machine as classifier can certainly improve the performance of automatic pain recognition system.

SURVEY OF BIOMETRIC SYSTEMS USING IRIS RECOGNITION

OpenAIRE

S.PON SANGEETHA; DR.M.KARNAN

2014-01-01

The security plays an important role in any type of organization in today’s life. Iris recognition is one of the leading automatic biometric systems in the area of security which is used to identify the individual person. Biometric systems include fingerprints, facial features, voice recognition, hand geometry, handwriting, the eye retina and the most secured one presented in this paper, the iris recognition. Biometric systems has become very famous in security systems because it is not possi...

Type-2 fuzzy graphical models for pattern recognition

CERN Document Server

Zeng, Jia

2015-01-01

This book discusses how to combine type-2 fuzzy sets and graphical models to solve a range of real-world pattern recognition problems such as speech recognition, handwritten Chinese character recognition, topic modeling as well as human action recognition. It covers these recent developments while also providing a comprehensive introduction to the fields of type-2 fuzzy sets and graphical models. Though primarily intended for graduate students, researchers and practitioners in fuzzy logic and pattern recognition, the book can also serve as a valuable reference work for researchers without any previous knowledge of these fields. Dr. Jia Zeng is a Professor at the School of Computer Science and Technology, Soochow University, China. Dr. Zhi-Qiang Liu is a Professor at the School of Creative Media, City University of Hong Kong, China.

Automatic Emotion Recognition in Speech: Possibilities and Significance

Directory of Open Access Journals (Sweden)

Milana Bojanić

2009-12-01

Full Text Available Automatic speech recognition and spoken language understanding are crucial steps towards a natural humanmachine interaction. The main task of the speech communication process is the recognition of the word sequence, but the recognition of prosody, emotion and stress tags may be of particular importance as well. This paper discusses thepossibilities of recognition emotion from speech signal in order to improve ASR, and also provides the analysis of acoustic features that can be used for the detection of speaker’s emotion and stress. The paper also provides a short overview of emotion and stress classification techniques. The importance and place of emotional speech recognition is shown in the domain of human-computer interactive systems and transaction communication model. The directions for future work are given at the end of this work.

8th International Conference on Computer Recognition Systems

CERN Document Server

Jackowski, Konrad; Kurzynski, Marek; Wozniak, Michał; Zolnierek, Andrzej

2013-01-01

The computer recognition systems are nowadays one of the most promising directions in artificial intelligence. This book is the most comprehensive study of this field. It contains a collection of 86 carefully selected articles contributed by experts of pattern recognition. It reports on current research with respect to both methodology and applications. In particular, it includes the following sections: Biometrics Data Stream Classification and Big Data Analytics  Features, learning, and classifiers Image processing and computer vision Medical applications Miscellaneous applications Pattern recognition and image processing in robotics  Speech and word recognition This book is a great reference tool for scientists who deal with the problems of designing computer pattern recognition systems. Its target readers can be the as well researchers as students of computer science, artificial intelligence or robotics.

Social context predicts recognition systems in ant queens

DEFF Research Database (Denmark)

Dreier, Stéphanie Agnès Jeanine; d'Ettorre, Patrizia

2009-01-01

Recognition of group-members is a key feature of sociality. Ants use chemical communication to discriminate nestmates from intruders, enhancing kin cooperation and preventing parasitism. The recognition code is embedded in their cuticular chemical profile, which typically varies between colonies....... We predicted that ants might be capable of accurate recognition in unusual situations when few individuals interact repeatedly, as new colonies started by two to three queens. Individual recognition would be favoured by selection when queens establish dominance hierarchies, because repeated fights...... for dominance are costly; but it would not evolve in absence of hierarchies. We previously showed that Pachycondyla co-founding queens, which form dominance hierarchies, have accurate individual recognition based on chemical cues. Here, we used the ant Lasius niger to test the null hypothesis that individual...

Smartphone based face recognition tool for the blind.

Science.gov (United States)

Kramer, K M; Hedin, D S; Rolkosky, D J

2010-01-01

The inability to identify people during group meetings is a disadvantage for blind people in many professional and educational situations. To explore the efficacy of face recognition using smartphones in these settings, we have prototyped and tested a face recognition tool for blind users. The tool utilizes Smartphone technology in conjunction with a wireless network to provide audio feedback of the people in front of the blind user. Testing indicated that the face recognition technology can tolerate up to a 40 degree angle between the direction a person is looking and the camera's axis and a 96% success rate with no false positives. Future work will be done to further develop the technology for local face recognition on the smartphone in addition to remote server based face recognition.

Recall and recognition hypermnesia for Socratic stimuli.

Science.gov (United States)

Kazén, Miguel; Solís-Macías, Víctor M

2016-01-01

In two experiments, we investigate hypermnesia, net memory improvements with repeated testing of the same material after a single study trial. In the first experiment, we found hypermnesia across three trials for the recall of word solutions to Socratic stimuli (dictionary-like definitions of concepts) replicating Erdelyi, Buschke, and Finkelstein and, for the first time using these materials, for their recognition. In the second experiment, we had two "yes/no" recognition groups, a Socratic stimuli group presented with concrete and abstract verbal materials and a word-only control group. Using signal detection measures, we found hypermnesia for concrete Socratic stimuli-and stable performance for abstract stimuli across three recognition tests. The control group showed memory decrements across tests. We interpret these findings with the alternative retrieval pathways (ARP) hypothesis, contrasting it with alternative theories of hypermnesia, such as depth of processing, generation and retrieve-recognise. We conclude that recognition hypermnesia for concrete Socratic stimuli is a reliable phenomenon, which we found in two experiments involving both forced-choice and yes/no recognition procedures.

Robustness-related issues in speaker recognition

CERN Document Server

Zheng, Thomas Fang

2017-01-01

This book presents an overview of speaker recognition technologies with an emphasis on dealing with robustness issues. Firstly, the book gives an overview of speaker recognition, such as the basic system framework, categories under different criteria, performance evaluation and its development history. Secondly, with regard to robustness issues, the book presents three categories, including environment-related issues, speaker-related issues and application-oriented issues. For each category, the book describes the current hot topics, existing technologies, and potential research focuses in the future. The book is a useful reference book and self-learning guide for early researchers working in the field of robust speech recognition.

Obligatory and facultative brain regions for voice-identity recognition.

Science.gov (United States)

Roswandowitz, Claudia; Kappes, Claudia; Obrig, Hellmuth; von Kriegstein, Katharina

2018-01-01

Recognizing the identity of others by their voice is an important skill for social interactions. To date, it remains controversial which parts of the brain are critical structures for this skill. Based on neuroimaging findings, standard models of person-identity recognition suggest that the right temporal lobe is the hub for voice-identity recognition. Neuropsychological case studies, however, reported selective deficits of voice-identity recognition in patients predominantly with right inferior parietal lobe lesions. Here, our aim was to work towards resolving the discrepancy between neuroimaging studies and neuropsychological case studies to find out which brain structures are critical for voice-identity recognition in humans. We performed a voxel-based lesion-behaviour mapping study in a cohort of patients (n = 58) with unilateral focal brain lesions. The study included a comprehensive behavioural test battery on voice-identity recognition of newly learned (voice-name, voice-face association learning) and familiar voices (famous voice recognition) as well as visual (face-identity recognition) and acoustic control tests (vocal-pitch and vocal-timbre discrimination). The study also comprised clinically established tests (neuropsychological assessment, audiometry) and high-resolution structural brain images. The three key findings were: (i) a strong association between voice-identity recognition performance and right posterior/mid temporal and right inferior parietal lobe lesions; (ii) a selective association between right posterior/mid temporal lobe lesions and voice-identity recognition performance when face-identity recognition performance was factored out; and (iii) an association of right inferior parietal lobe lesions with tasks requiring the association between voices and faces but not voices and names. The results imply that the right posterior/mid temporal lobe is an obligatory structure for voice-identity recognition, while the inferior parietal lobe is

Conceptual fluency at test shifts recognition response bias in Alzheimer's disease: implications for increased false recognition.

Science.gov (United States)

Gold, Carl A; Marchant, Natalie L; Koutstaal, Wilma; Schacter, Daniel L; Budson, Andrew E

2007-09-20

The presence or absence of conceptual information in pictorial stimuli may explain the mixed findings of previous studies of false recognition in patients with mild Alzheimer's disease (AD). To test this hypothesis, 48 patients with AD were compared to 48 healthy older adults on a recognition task first described by Koutstaal et al. [Koutstaal, W., Reddy, C., Jackson, E. M., Prince, S., Cendan, D. L., & Schacter D. L. (2003). False recognition of abstract versus common objects in older and younger adults: Testing the semantic categorization account. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 499-510]. Participants studied and were tested on their memory for categorized ambiguous pictures of common objects. The presence of conceptual information at study and/or test was manipulated by providing or withholding disambiguating semantic labels. Analyses focused on testing two competing theories. The semantic encoding hypothesis, which posits that the inter-item perceptual details are not encoded by AD patients when conceptual information is present in the stimuli, was not supported by the findings. In contrast, the conceptual fluency hypothesis was supported. Enhanced conceptual fluency at test dramatically shifted AD patients to a more liberal response bias, raising their false recognition. These results suggest that patients with AD rely on the fluency of test items in making recognition memory decisions. We speculate that AD patients' over reliance upon fluency may be attributable to (1) dysfunction of the hippocampus, disrupting recollection, and/or (2) dysfunction of prefrontal cortex, disrupting post-retrieval processes.

Cross-modal individual recognition in wild African lions.

Science.gov (United States)

Gilfillan, Geoffrey; Vitale, Jessica; McNutt, John Weldon; McComb, Karen

2016-08-01

Individual recognition is considered to have been fundamental in the evolution of complex social systems and is thought to be a widespread ability throughout the animal kingdom. Although robust evidence for individual recognition remains limited, recent experimental paradigms that examine cross-modal processing have demonstrated individual recognition in a range of captive non-human animals. It is now highly relevant to test whether cross-modal individual recognition exists within wild populations and thus examine how it is employed during natural social interactions. We address this question by testing audio-visual cross-modal individual recognition in wild African lions (Panthera leo) using an expectancy-violation paradigm. When presented with a scenario where the playback of a loud-call (roaring) broadcast from behind a visual block is incongruent with the conspecific previously seen there, subjects responded more strongly than during the congruent scenario where the call and individual matched. These findings suggest that lions are capable of audio-visual cross-modal individual recognition and provide a useful method for studying this ability in wild populations. © 2016 The Author(s).

On speech recognition during anaesthesia

DEFF Research Database (Denmark)

Alapetite, Alexandre

2007-01-01

This PhD thesis in human-computer interfaces (informatics) studies the case of the anaesthesia record used during medical operations and the possibility to supplement it with speech recognition facilities. Problems and limitations have been identified with the traditional paper-based anaesthesia...... and inaccuracies in the anaesthesia record. Supplementing the electronic anaesthesia record interface with speech input facilities is proposed as one possible solution to a part of the problem. The testing of the various hypotheses has involved the development of a prototype of an electronic anaesthesia record...... interface with speech input facilities in Danish. The evaluation of the new interface was carried out in a full-scale anaesthesia simulator. This has been complemented by laboratory experiments on several aspects of speech recognition for this type of use, e.g. the effects of noise on speech recognition...

Multispectral Palmprint Recognition Using a Quaternion Matrix

Directory of Open Access Journals (Sweden)

Yafeng Li

2012-04-01

Full Text Available Palmprints have been widely studied for biometric recognition for many years. Traditionally, a white light source is used for illumination. Recently, multispectral imaging has drawn attention because of its high recognition accuracy. Multispectral palmprint systems can provide more discriminant information under different illuminations in a short time, thus they can achieve better recognition accuracy. Previously, multispectral palmprint images were taken as a kind of multi-modal biometrics, and the fusion scheme on the image level or matching score level was used. However, some spectral information will be lost during image level or matching score level fusion. In this study, we propose a new method for multispectral images based on a quaternion model which could fully utilize the multispectral information. Firstly, multispectral palmprint images captured under red, green, blue and near-infrared (NIR illuminations were represented by a quaternion matrix, then principal component analysis (PCA and discrete wavelet transform (DWT were applied respectively on the matrix to extract palmprint features. After that, Euclidean distance was used to measure the dissimilarity between different features. Finally, the sum of two distances and the nearest neighborhood classifier were employed for recognition decision. Experimental results showed that using the quaternion matrix can achieve a higher recognition rate. Given 3000 test samples from 500 palms, the recognition rate can be as high as 98.83%.

The hierarchical brain network for face recognition.

Science.gov (United States)

Zhen, Zonglei; Fang, Huizhen; Liu, Jia

2013-01-01

Numerous functional magnetic resonance imaging (fMRI) studies have identified multiple cortical regions that are involved in face processing in the human brain. However, few studies have characterized the face-processing network as a functioning whole. In this study, we used fMRI to identify face-selective regions in the entire brain and then explore the hierarchical structure of the face-processing network by analyzing functional connectivity among these regions. We identified twenty-five regions mainly in the occipital, temporal and frontal cortex that showed a reliable response selective to faces (versus objects) across participants and across scan sessions. Furthermore, these regions were clustered into three relatively independent sub-networks in a face-recognition task on the basis of the strength of functional connectivity among them. The functionality of the sub-networks likely corresponds to the recognition of individual identity, retrieval of semantic knowledge and representation of emotional information. Interestingly, when the task was switched to object recognition from face recognition, the functional connectivity between the inferior occipital gyrus and the rest of the face-selective regions were significantly reduced, suggesting that this region may serve as an entry node in the face-processing network. In sum, our study provides empirical evidence for cognitive and neural models of face recognition and helps elucidate the neural mechanisms underlying face recognition at the network level.

Efficient CEPSTRAL Normalization for Robust Speech Recognition

National Research Council Canada - National Science Library

Liu, Fu-Hua; Stern, Richard M; Huang, Xuedong; Acero, Alejandro

1993-01-01

In this paper we describe and compare the performance of a series of cepstrum-based procedures that enable the CMU SPHINX-II speech recognition system to maintain a high level of recognition accuracy...

On-device mobile speech recognition

OpenAIRE

Mustafa, MK

2016-01-01

Despite many years of research, Speech Recognition remains an active area of research in Artificial Intelligence. Currently, the most common commercial application of this technology on mobile devices uses a wireless client – server approach to meet the computational and memory demands of the speech recognition process. Unfortunately, such an approach is unlikely to remain viable when fully applied over the approximately 7.22 Billion mobile phones currently in circulation. In this thesis we p...

Culture/Religion and Identity: Social Justice versus Recognition

Science.gov (United States)

Bekerman, Zvi

2012-01-01

Recognition is the main word attached to multicultural perspectives. The multicultural call for recognition, the one calling for the recognition of cultural minorities and identities, the one now voiced by liberal states all over and also in Israel was a more difficult one. It took the author some time to realize that calling for the recognition…

Foreign language learning, hyperlexia, and early word recognition.

Science.gov (United States)

Sparks, R L; Artzer, M

2000-01-01

Children with hyperlexia read words spontaneously before the age of five, have impaired comprehension on both listening and reading tasks, and have word recognition skill above expectations based on cognitive and linguistic abilities. One student with hyperlexia and another student with higher word recognition than comprehension skills who started to read words at a very early age were followed over several years from the primary grades through high school when both were completing a second-year Spanish course. The purpose of the present study was to examine the foreign language (FL) word recognition, spelling, reading comprehension, writing, speaking, and listening skills of the two students and another high school student without hyperlexia. Results showed that the student without hyperlexia achieved higher scores than the hyperlexic student and the student with above average word recognition skills on most FL proficiency measures. The student with hyperlexia and the student with above average word recognition skills achieved higher scores on the Spanish proficiency tasks that required the exclusive use of phonological (pronunciation) and phonological/orthographic (word recognition, spelling) skills than on Spanish proficiency tasks that required the use of listening comprehension and speaking and writing skills. The findings provide support for the notion that word recognition and spelling in a FL may be modular processes and exist independently of general cognitive and linguistic skills. Results also suggest that students may have stronger FL learning skills in one language component than in other components of language, and that there may be a weak relationship between FL word recognition and oral proficiency in the FL.

MEMBRAIN NEURAL NETWORK FOR VISUAL PATTERN RECOGNITION

Directory of Open Access Journals (Sweden)

Artur Popko

2013-06-01

Full Text Available Recognition of visual patterns is one of significant applications of Artificial Neural Networks, which partially emulate human thinking in the domain of artificial intelligence. In the paper, a simplified neural approach to recognition of visual patterns is portrayed and discussed. This paper is dedicated for investigators in visual patterns recognition, Artificial Neural Networking and related disciplines. The document describes also MemBrain application environment as a powerful and easy to use neural networks’ editor and simulator supporting ANN.

Chiral recognition in separation science: an overview.

Science.gov (United States)

Scriba, Gerhard K E

2013-01-01

Chiral recognition phenomena play an important role in nature as well as analytical separation sciences. In separation sciences such as chromatography and capillary electrophoresis, enantiospecific interactions between the enantiomers of an analyte and the chiral selector are required in order to observe enantioseparations. Due to the large structural variety of chiral selectors applied, different mechanisms and structural features contribute to the chiral recognition process. This chapter briefly illustrates the current models of the enantiospecific recognition on the structural basics of various chiral selectors.

L2 Word Recognition: Influence of L1 Orthography on Multi-syllabic Word Recognition.

Science.gov (United States)

Hamada, Megumi

2017-10-01

L2 reading research suggests that L1 orthographic experience influences L2 word recognition. Nevertheless, the findings on multi-syllabic words in English are still limited despite the fact that a vast majority of words are multi-syllabic. The study investigated whether L1 orthography influences the recognition of multi-syllabic words, focusing on the position of an embedded word. The participants were Arabic ESL learners, Chinese ESL learners, and native speakers of English. The task was a word search task, in which the participants identified a target word embedded in a pseudoword at the initial, middle, or final position. The search accuracy and speed indicated that all groups showed a strong preference for the initial position. The accuracy data further indicated group differences. The Arabic group showed higher accuracy in the final than middle, while the Chinese group showed the opposite and the native speakers showed no difference between the two positions. The findings suggest that L2 multi-syllabic word recognition involves unique processes.

77 FR 9590 - Recognition and Accreditation

Science.gov (United States)

2012-02-17

..., or social service purpose? 5. Discussion of withdrawal of recognition. Are the current procedures for withdrawal of recognition for an organization effective? See 8 CFR 1292.2(c). If not, how can the process be... services in order to ensure that they are serving a non-profit, religious, charitable, or social service...

Hybrid Speaker Recognition Using Universal Acoustic Model

Science.gov (United States)

Nishimura, Jun; Kuroda, Tadahiro

We propose a novel speaker recognition approach using a speaker-independent universal acoustic model (UAM) for sensornet applications. In sensornet applications such as “Business Microscope”, interactions among knowledge workers in an organization can be visualized by sensing face-to-face communication using wearable sensor nodes. In conventional studies, speakers are detected by comparing energy of input speech signals among the nodes. However, there are often synchronization errors among the nodes which degrade the speaker recognition performance. By focusing on property of the speaker's acoustic channel, UAM can provide robustness against the synchronization error. The overall speaker recognition accuracy is improved by combining UAM with the energy-based approach. For 0.1s speech inputs and 4 subjects, speaker recognition accuracy of 94% is achieved at the synchronization error less than 100ms.

A Survey of 2D Face Recognition Techniques

Directory of Open Access Journals (Sweden)

Mejda Chihaoui

2016-09-01

Full Text Available Despite the existence of various biometric techniques, like fingerprints, iris scan, as well as hand geometry, the most efficient and more widely-used one is face recognition. This is because it is inexpensive, non-intrusive and natural. Therefore, researchers have developed dozens of face recognition techniques over the last few years. These techniques can generally be divided into three categories, based on the face data processing methodology. There are methods that use the entire face as input data for the proposed recognition system, methods that do not consider the whole face, but only some features or areas of the face and methods that use global and local face characteristics simultaneously. In this paper, we present an overview of some well-known methods in each of these categories. First, we expose the benefits of, as well as the challenges to the use of face recognition as a biometric tool. Then, we present a detailed survey of the well-known methods by expressing each method’s principle. After that, a comparison between the three categories of face recognition techniques is provided. Furthermore, the databases used in face recognition are mentioned, and some results of the applications of these methods on face recognition databases are presented. Finally, we highlight some new promising research directions that have recently appeared.

Superpixel-Based Feature for Aerial Image Scene Recognition

Directory of Open Access Journals (Sweden)

Hongguang Li

2018-01-01

Full Text Available Image scene recognition is a core technology for many aerial remote sensing applications. Different landforms are inputted as different scenes in aerial imaging, and all landform information is regarded as valuable for aerial image scene recognition. However, the conventional features of the Bag-of-Words model are designed using local points or other related information and thus are unable to fully describe landform areas. This limitation cannot be ignored when the aim is to ensure accurate aerial scene recognition. A novel superpixel-based feature is proposed in this study to characterize aerial image scenes. Then, based on the proposed feature, a scene recognition method of the Bag-of-Words model for aerial imaging is designed. The proposed superpixel-based feature that utilizes landform information establishes top-task superpixel extraction of landforms to bottom-task expression of feature vectors. This characterization technique comprises the following steps: simple linear iterative clustering based superpixel segmentation, adaptive filter bank construction, Lie group-based feature quantification, and visual saliency model-based feature weighting. Experiments of image scene recognition are carried out using real image data captured by an unmanned aerial vehicle (UAV. The recognition accuracy of the proposed superpixel-based feature is 95.1%, which is higher than those of scene recognition algorithms based on other local features.

Features fusion based approach for handwritten Gujarati character recognition

Directory of Open Access Journals (Sweden)

Ankit Sharma

2017-02-01

Full Text Available Handwritten character recognition is a challenging area of research. Lots of research activities in the area of character recognition are already done for Indian languages such as Hindi, Bangla, Kannada, Tamil and Telugu. Literature review on handwritten character recognition indicates that in comparison with other Indian scripts research activities on Gujarati handwritten character recognition are very less.  This paper aims to bring Gujarati character recognition in attention. Recognition of isolated Gujarati handwritten characters is proposed using three different kinds of features and their fusion. Chain code based, zone based and projection profiles based features are utilized as individual features. One of the significant contribution of proposed work is towards the generation of large and representative dataset of 88,000 handwritten Gujarati characters. Experiments are carried out on this developed dataset. Artificial Neural Network (ANN, Support Vector Machine (SVM and Naive Bayes (NB classifier based methods are implemented for handwritten Gujarati character recognition. Experimental results show substantial enhancement over state-of-the-art and authenticate our proposals.

Material recognition based on thermal cues: Mechanisms and applications.

Science.gov (United States)

Ho, Hsin-Ni

2018-01-01

Some materials feel colder to the touch than others, and we can use this difference in perceived coldness for material recognition. This review focuses on the mechanisms underlying material recognition based on thermal cues. It provides an overview of the physical, perceptual, and cognitive processes involved in material recognition. It also describes engineering domains in which material recognition based on thermal cues have been applied. This includes haptic interfaces that seek to reproduce the sensations associated with contact in virtual environments and tactile sensors aim for automatic material recognition. The review concludes by considering the contributions of this line of research in both science and engineering.

Wood Variety Recognition on Mobile Devices

Czech Academy of Sciences Publication Activity Database

Vácha, Pavel; Haindl, Michal

2013-01-01

Roč. 2013, č. 93 (2013), s. 52-52 ISSN 0926-4981 R&D Projects: GA ČR GA102/08/0593 Institutional support: RVO:67985556 Keywords : wood recognition * Markov random fields Subject RIV: BD - Theory of Information http://library.utia.cas.cz/separaty/2013/RO/vacha-wood variety recognition on mobile devices.pdf

Embedded Face Detection and Recognition

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Göksel Günlü

2012-10-01

Full Text Available The need to increase security in open or public spaces has in turn given rise to the requirement to monitor these spaces and analyse those images on-site and on-time. At this point, the use of smart cameras – of which the popularity has been increasing – is one step ahead. With sensors and Digital Signal Processors (DSPs, smart cameras generate ad hoc results by analysing the numeric images transmitted from the sensor by means of a variety of image-processing algorithms. Since the images are not transmitted to a distance processing unit but rather are processed inside the camera, it does not necessitate high-bandwidth networks or high processor powered systems; it can instantaneously decide on the required access. Nonetheless, on account of restricted memory, processing power and overall power, image processing algorithms need to be developed and optimized for embedded processors. Among these algorithms, one of the most important is for face detection and recognition. A number of face detection and recognition methods have been proposed recently and many of these methods have been tested on general-purpose processors. In smart cameras – which are real-life applications of such methods – the widest use is on DSPs. In the present study, the Viola-Jones face detection method – which was reported to run faster on PCs – was optimized for DSPs; the face recognition method was combined with the developed sub-region and mask-based DCT (Discrete Cosine Transform. As the employed DSP is a fixed-point processor, the processes were performed with integers insofar as it was possible. To enable face recognition, the image was divided into sub-regions and from each sub-region the robust coefficients against disruptive elements – like face expression, illumination, etc. – were selected as the features. The discrimination of the selected features was enhanced via LDA (Linear Discriminant Analysis and then employed for recognition. Thanks to its

Recognition of social identity in ants

Directory of Open Access Journals (Sweden)

Nick eBos

2012-03-01

Full Text Available Recognizing the identity of others, from the individual to the group level, is a hallmark of society. Ants, and other social insects, have evolved advanced societies characterized by efficient social recognition systems. Colony identity is mediated by colony specific signature mixtures, a blend of hydrocarbons present on the cuticle of every individual (the label. Recognition occurs when an ant encounters another individual, and compares the label it perceives to an internal representation of its own colony odor (the template. A mismatch between label and template leads to rejection of the encountered individual. Although advances have been made in our understanding of how the label is produced and acquired, contradictory evidence exists about information processing of recognition cues. Here, we review the literature on template acquisition in ants and address how and when the template is formed, where in the nervous system it is localized, and the possible role of learning. We combine seemingly contradictory evidence in to a novel, parsimonious theory for the information processing of nestmate recognition cues.

Face averages enhance user recognition for smartphone security.

Science.gov (United States)

Robertson, David J; Kramer, Robin S S; Burton, A Mike

2015-01-01

Our recognition of familiar faces is excellent, and generalises across viewing conditions. However, unfamiliar face recognition is much poorer. For this reason, automatic face recognition systems might benefit from incorporating the advantages of familiarity. Here we put this to the test using the face verification system available on a popular smartphone (the Samsung Galaxy). In two experiments we tested the recognition performance of the smartphone when it was encoded with an individual's 'face-average'--a representation derived from theories of human face perception. This technique significantly improved performance for both unconstrained celebrity images (Experiment 1) and for real faces (Experiment 2): users could unlock their phones more reliably when the device stored an average of the user's face than when they stored a single image. This advantage was consistent across a wide variety of everyday viewing conditions. Furthermore, the benefit did not reduce the rejection of imposter faces. This benefit is brought about solely by consideration of suitable representations for automatic face recognition, and we argue that this is just as important as development of matching algorithms themselves. We propose that this representation could significantly improve recognition rates in everyday settings.

Low-contrast underwater living fish recognition using PCANet

Science.gov (United States)

Sun, Xin; Yang, Jianping; Wang, Changgang; Dong, Junyu; Wang, Xinhua

2018-04-01

Quantitative and statistical analysis of ocean creatures is critical to ecological and environmental studies. And living fish recognition is one of the most essential requirements for fishery industry. However, light attenuation and scattering phenomenon are present in the underwater environment, which makes underwater images low-contrast and blurry. This paper tries to design a robust framework for accurate fish recognition. The framework introduces a two stage PCA Network to extract abstract features from fish images. On a real-world fish recognition dataset, we use a linear SVM classifier and set penalty coefficients to conquer data unbalanced issue. Feature visualization results show that our method can avoid the feature distortion in boundary regions of underwater image. Experiments results show that the PCA Network can extract discriminate features and achieve promising recognition accuracy. The framework improves the recognition accuracy of underwater living fishes and can be easily applied to marine fishery industry.

Food brand recognition and BMI in preschoolers.

Science.gov (United States)

Harrison, Kristen; Moorman, Jessica; Peralta, Mericarmen; Fayhee, Kally

2017-07-01

Children's food brand recognition predicts health-related outcomes such as preference for obesogenic foods and increased risk for overweight. However, it is uncertain to what degree food brand recognition acts as a proxy for other factors such as parental education and income, child vocabulary, child age, child race/ethnicity, parent healthy eating guidance, child commercial TV viewing, and child dietary intake, all of which may influence or be influenced by food brand recognition. U.S. preschoolers (N = 247, average age 56 months) were measured for BMI and completed the Peabody Picture Vocabulary Test plus recognition and recall measures for a selection of U.S. food brands. Parents completed measures of healthy eating guidance, child dietary intake, child commercial TV viewing, parent education, household income, parent BMI, and child age and race/ethnicity. Controlling these variables, child food brand recognition predicted higher child BMI percentile. Further, qualitative examination of children's incorrect answers to recall items demonstrated perceptual confusion between brand mascots and other fantasy characters to which children are exposed during the preschool years, extending theory on child consumer development. Copyright © 2017 Elsevier Ltd. All rights reserved.

AN ILLUMINATION INVARIANT TEXTURE BASED FACE RECOGNITION

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K. Meena

2013-11-01

Full Text Available Automatic face recognition remains an interesting but challenging computer vision open problem. Poor illumination is considered as one of the major issue, since illumination changes cause large variation in the facial features. To resolve this, illumination normalization preprocessing techniques are employed in this paper to enhance the face recognition rate. The methods such as Histogram Equalization (HE, Gamma Intensity Correction (GIC, Normalization chain and Modified Homomorphic Filtering (MHF are used for preprocessing. Owing to great success, the texture features are commonly used for face recognition. But these features are severely affected by lighting changes. Hence texture based models Local Binary Pattern (LBP, Local Derivative Pattern (LDP, Local Texture Pattern (LTP and Local Tetra Patterns (LTrPs are experimented under different lighting conditions. In this paper, illumination invariant face recognition technique is developed based on the fusion of illumination preprocessing with local texture descriptors. The performance has been evaluated using YALE B and CMU-PIE databases containing more than 1500 images. The results demonstrate that MHF based normalization gives significant improvement in recognition rate for the face images with large illumination conditions.

The optimal viewing position in face recognition.

Science.gov (United States)

Hsiao, Janet H; Liu, Tina T

2012-02-28

In English word recognition, the best recognition performance is usually obtained when the initial fixation is directed to the left of the center (optimal viewing position, OVP). This effect has been argued to involve an interplay of left hemisphere lateralization for language processing and the perceptual experience of fixating at word beginnings most often. While both factors predict a left-biased OVP in visual word recognition, in face recognition they predict contrasting biases: People prefer to fixate the left half-face, suggesting that the OVP should be to the left of the center; nevertheless, the right hemisphere lateralization in face processing suggests that the OVP should be to the right of the center in order to project most of the face to the right hemisphere. Here, we show that the OVP in face recognition was to the left of the center, suggesting greater influence from the perceptual experience than hemispheric asymmetry in central vision. In contrast, hemispheric lateralization effects emerged when faces were presented away from the center; there was an interaction between presented visual field and location (center vs. periphery), suggesting differential influence from perceptual experience and hemispheric asymmetry in central and peripheral vision.

Mapping correspondence between facial mimicry and emotion recognition in healthy subjects.

Science.gov (United States)

Ponari, Marta; Conson, Massimiliano; D'Amico, Nunzia Pina; Grossi, Dario; Trojano, Luigi

2012-12-01

We aimed at verifying the hypothesis that facial mimicry is causally and selectively involved in emotion recognition. For this purpose, in Experiment 1, we explored the effect of tonic contraction of muscles in upper or lower half of participants' face on their ability to recognize emotional facial expressions. We found that the "lower" manipulation specifically impaired recognition of happiness and disgust, the "upper" manipulation impaired recognition of anger, while both manipulations affected recognition of fear; recognition of surprise and sadness were not affected by either blocking manipulations. In Experiment 2, we verified whether emotion recognition is hampered by stimuli in which an upper or lower half-face showing an emotional expression is combined with a neutral half-face. We found that the neutral lower half-face interfered with recognition of happiness and disgust, whereas the neutral upper half impaired recognition of anger; recognition of fear and sadness was impaired by both manipulations, whereas recognition of surprise was not affected by either manipulation. Taken together, the present findings support simulation models of emotion recognition and provide insight into the role of mimicry in comprehension of others' emotional facial expressions. PsycINFO Database Record (c) 2012 APA, all rights reserved.

Facial Expression Recognition Based on TensorFlow Platform

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Xia Xiao-Ling

2017-01-01

Full Text Available Facial expression recognition have a wide range of applications in human-machine interaction, pattern recognition, image understanding, machine vision and other fields. Recent years, it has gradually become a hot research. However, different people have different ways of expressing their emotions, and under the influence of brightness, background and other factors, there are some difficulties in facial expression recognition. In this paper, based on the Inception-v3 model of TensorFlow platform, we use the transfer learning techniques to retrain facial expression dataset (The Extended Cohn-Kanade dataset, which can keep the accuracy of recognition and greatly reduce the training time.

Picture languages formal models for picture recognition

CERN Document Server

Rosenfeld, Azriel

1979-01-01

Computer Science and Applied Mathematics: Picture Languages: Formal Models for Picture Recognition treats pictorial pattern recognition from the formal standpoint of automata theory. This book emphasizes the capabilities and relative efficiencies of two types of automata-array automata and cellular array automata, with respect to various array recognition tasks. The array automata are simple processors that perform sequences of operations on arrays, while the cellular array automata are arrays of processors that operate on pictures in a highly parallel fashion, one processor per picture element. This compilation also reviews a collection of results on two-dimensional sequential and parallel array acceptors. Some of the analogous one-dimensional results and array grammars and their relation to acceptors are likewise covered in this text. This publication is suitable for researchers, professionals, and specialists interested in pattern recognition and automata theory.

Control of Target Molecular Recognition in a Small Pore Space with Biomolecule-Recognition Gating Membrane.

Science.gov (United States)

Okuyama, Hiroto; Oshiba, Yuhei; Ohashi, Hidenori; Yamaguchi, Takeo

2018-05-01

A biomolecule-recognition gating membrane, which introduces thermosensitive graft polymer including molecular recognition receptor into porous membrane substrate, can close its pores by recognizing target biomolecule. The present study reports strategies for improving both versatility and sensitivity of the gating membrane. First, the membrane is fabricated by introducing the receptor via a selectively reactive click reaction improving the versatility. Second, the sensitivity of the membrane is enhanced via an active delivering method of the target molecules into the pores. In the method, the tiny signal of the target biomolecule is amplified as obvious pressure change. Furthermore, this offers 15 times higher sensitivity compared to the previously reported passive delivering method (membrane immersion to sample solution) with significantly shorter recognition time. The improvement will aid in applying the gating membrane to membrane sensors in medical fields. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

CASE Recognition Awards.

Science.gov (United States)

Currents, 1985

1985-01-01

A total of 294 schools, colleges, and universities received prizes in this year's CASE Recognition program. Awards were given in: public relations programs, student recruitment, marketing, program pulications, news writing, fund raising, radio programming, school periodicals, etc. (MLW)

Forensic speaker recognition

NARCIS (Netherlands)

Meuwly, Didier

2013-01-01

The aim of forensic speaker recognition is to establish links between individuals and criminal activities, through audio speech recordings. This field is multidisciplinary, combining predominantly phonetics, linguistics, speech signal processing, and forensic statistics. On these bases, expert-based

Object Recognition and Localization: The Role of Tactile Sensors

Directory of Open Access Journals (Sweden)

Achint Aggarwal

2014-02-01

Full Text Available Tactile sensors, because of their intrinsic insensitivity to lighting conditions and water turbidity, provide promising opportunities for augmenting the capabilities of vision sensors in applications involving object recognition and localization. This paper presents two approaches for haptic object recognition and localization for ground and underwater environments. The first approach called Batch Ransac and Iterative Closest Point augmented Particle Filter (BRICPPF is based on an innovative combination of particle filters, Iterative-Closest-Point algorithm, and a feature-based Random Sampling and Consensus (RANSAC algorithm for database matching. It can handle a large database of 3D-objects of complex shapes and performs a complete six-degree-of-freedom localization of static objects. The algorithms are validated by experimentation in ground and underwater environments using real hardware. To our knowledge this is the first instance of haptic object recognition and localization in underwater environments. The second approach is biologically inspired, and provides a close integration between exploration and recognition. An edge following exploration strategy is developed that receives feedback from the current state of recognition. A recognition by parts approach is developed which uses the BRICPPF for object sub-part recognition. Object exploration is either directed to explore a part until it is successfully recognized, or is directed towards new parts to endorse the current recognition belief. This approach is validated by simulation experiments.

Discriminative learning for speech recognition

CERN Document Server

He, Xiadong

2008-01-01

In this book, we introduce the background and mainstream methods of probabilistic modeling and discriminative parameter optimization for speech recognition. The specific models treated in depth include the widely used exponential-family distributions and the hidden Markov model. A detailed study is presented on unifying the common objective functions for discriminative learning in speech recognition, namely maximum mutual information (MMI), minimum classification error, and minimum phone/word error. The unification is presented, with rigorous mathematical analysis, in a common rational-functio

Brain Structural Correlates of Emotion Recognition in Psychopaths.

Directory of Open Access Journals (Sweden)

Vanessa Pera-Guardiola

Full Text Available Individuals with psychopathy present deficits in the recognition of facial emotional expressions. However, the nature and extent of these alterations are not fully understood. Furthermore, available data on the functional neural correlates of emotional face recognition deficits in adult psychopaths have provided mixed results. In this context, emotional face morphing tasks may be suitable for clarifying mild and emotion-specific impairments in psychopaths. Likewise, studies exploring corresponding anatomical correlates may be useful for disentangling available neurofunctional evidence based on the alleged neurodevelopmental roots of psychopathic traits. We used Voxel-Based Morphometry and a morphed emotional face expression recognition task to evaluate the relationship between regional gray matter (GM volumes and facial emotion recognition deficits in male psychopaths. In comparison to male healthy controls, psychopaths showed deficits in the recognition of sad, happy and fear emotional expressions. In subsequent brain imaging analyses psychopaths with better recognition of facial emotional expressions showed higher volume in the prefrontal cortex (orbitofrontal, inferior frontal and dorsomedial prefrontal cortices, somatosensory cortex, anterior insula, cingulate cortex and the posterior lobe of the cerebellum. Amygdala and temporal lobe volumes contributed to better emotional face recognition in controls only. These findings provide evidence suggesting that variability in brain morphometry plays a role in accounting for psychopaths' impaired ability to recognize emotional face expressions, and may have implications for comprehensively characterizing the empathy and social cognition dysfunctions typically observed in this population of subjects.

Use of digital speech recognition in diagnostics radiology

International Nuclear Information System (INIS)

Arndt, H.; Stockheim, D.; Mutze, S.; Petersein, J.; Gregor, P.; Hamm, B.

1999-01-01

Purpose: Applicability and benefits of digital speech recognition in diagnostic radiology were tested using the speech recognition system SP 6000. Methods: The speech recognition system SP 6000 was integrated into the network of the institute and connected to the existing Radiological Information System (RIS). Three subjects used this system for writing 2305 findings from dictation. After the recognition process the date, length of dictation, time required for checking/correction, kind of examination and error rate were recorded for every dictation. With the same subjects, a correlation was performed with 625 conventionally written finding. Results: After an 1-hour initial training the average error rates were 8.4 to 13.3%. The first adaptation of the speech recognition system (after nine days) decreased the average error rates to 2.4 to 10.7% due to the ability of the program to learn. The 2 nd and 3 rd adaptations resulted only in small changes of the error rate. An individual comparison of the error rate developments in the same kind of investigation showed the relative independence of the error rate on the individual user. Conclusion: The results show that the speech recognition system SP 6000 can be evaluated as an advantageous alternative for quickly recording radiological findings. A comparison between manually writing and dictating the findings verifies the individual differences of the writing speeds and shows the advantage of the application of voice recognition when faced with normal keyboard performance. (orig.) [de

Recognition memory probes affect what is remembered in schizophrenia.

Science.gov (United States)

Schwartz, Barbara L; Parker, Elizabeth S; Rosse, Richard B; Deutsch, Stephen I

2009-05-15

Cognitive psychology offers tools to localize the memory processes most vulnerable to disruption in schizophrenia and to identify how patients with schizophrenia best remember. In this research, we used the University of Southern California Repeatable Episodic Memory Test (USC-REMT; Parker, E.S., Landau, S.M., Whipple, S.C., Schwartz, B.L., 2004. Aging, recall, and recognition: A study on the sensitivity of the University of Southern California Repeatable Episodic Memory Test (USC-REMT). Journal of Clinical and Experimental Neuropsychology 26(3), 428-440.) to examine how two different recognition memory probes affect memory performance in patients with schizophrenia and matched controls. Patients with schizophrenia studied equivalent word lists and were tested by yes-no recognition and forced-choice recognition following identical encoding and storage conditions. Compared with controls, patients with schizophrenia were particularly impaired when tested by yes-no recognition relative to forced-choice recognition. Patients had greatest deficits on hits in yes-no recognition but did not exhibit elevated false alarms. The data point to the importance of retrieval processes in schizophrenia, and highlight the need for further research on ways to help patients with schizophrenia access what they have learned.

Implementation of perceptual aspects in a face recognition algorithm

International Nuclear Information System (INIS)

Crenna, F; Bovio, L; Rossi, G B; Zappa, E; Testa, R; Gasparetto, M

2013-01-01

Automatic face recognition is a biometric technique particularly appreciated in security applications. In fact face recognition presents the opportunity to operate at a low invasive level without the collaboration of the subjects under tests, with face images gathered either from surveillance systems or from specific cameras located in strategic points. The automatic recognition algorithms perform a measurement, on the face images, of a set of specific characteristics of the subject and provide a recognition decision based on the measurement results. Unfortunately several quantities may influence the measurement of the face geometry such as its orientation, the lighting conditions, the expression and so on, affecting the recognition rate. On the other hand human recognition of face is a very robust process far less influenced by the surrounding conditions. For this reason it may be interesting to insert perceptual aspects in an automatic facial-based recognition algorithm to improve its robustness. This paper presents a first study in this direction investigating the correlation between the results of a perception experiment and the facial geometry, estimated by means of the position of a set of repere points

Multiple Levels of Recognition in Ants: A Feature of Complex Societies

DEFF Research Database (Denmark)

D'Ettorre, Patrizia

2008-01-01

diverse. In ants, social interactions are regulated by at least three levels of recognition. Nestmate recognition occurs between colonies, is very effective, and involves fast processing. Within a colony, division of labor is enhanced by recognition of different classes of individuals. Ultimately......, in particular circumstances, such as cooperative colony founding with stable dominance hierarchies, ants are capable of individual recognition. The underlying recognition cues and mechanisms appear to be specific to each recognition level, and their integrated understanding could contribute...

Recognition-Based Pedagogy: Teacher Candidates' Experience of Deficit

Science.gov (United States)

Parkison, Paul T.; DaoJensen, Thuy

2014-01-01

This study seeks to introduce what we call "recognition-based pedagogy" as a conceptual frame through which teachers and instructors can collaboratively develop educative experiences with students. Recognition-based pedagogy connects the theories of critical pedagogy, identity politics, and the politics of recognition with the educative…

DNA recognition by synthetic constructs.

Science.gov (United States)

Pazos, Elena; Mosquera, Jesús; Vázquez, M Eugenio; Mascareñas, José L

2011-09-05

The interaction of transcription factors with specific DNA sites is key for the regulation of gene expression. Despite the availability of a large body of structural data on protein-DNA complexes, we are still far from fully understanding the molecular and biophysical bases underlying such interactions. Therefore, the development of non-natural agents that can reproduce the DNA-recognition properties of natural transcription factors remains a major and challenging goal in chemical biology. In this review we summarize the basics of double-stranded DNA recognition by transcription factors, and describe recent developments in the design and preparation of synthetic DNA binders. We mainly focus on synthetic peptides that have been designed by following the DNA interaction of natural proteins, and we discuss how the tools of organic synthesis can be used to make artificial constructs equipped with functionalities that introduce additional properties to the recognition process, such as sensing and controllability. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Utterance independent bimodal emotion recognition in spontaneous communication

Science.gov (United States)

Tao, Jianhua; Pan, Shifeng; Yang, Minghao; Li, Ya; Mu, Kaihui; Che, Jianfeng

2011-12-01

Emotion expressions sometimes are mixed with the utterance expression in spontaneous face-to-face communication, which makes difficulties for emotion recognition. This article introduces the methods of reducing the utterance influences in visual parameters for the audio-visual-based emotion recognition. The audio and visual channels are first combined under a Multistream Hidden Markov Model (MHMM). Then, the utterance reduction is finished by finding the residual between the real visual parameters and the outputs of the utterance related visual parameters. This article introduces the Fused Hidden Markov Model Inversion method which is trained in the neutral expressed audio-visual corpus to solve the problem. To reduce the computing complexity the inversion model is further simplified to a Gaussian Mixture Model (GMM) mapping. Compared with traditional bimodal emotion recognition methods (e.g., SVM, CART, Boosting), the utterance reduction method can give better results of emotion recognition. The experiments also show the effectiveness of our emotion recognition system when it was used in a live environment.

Fingerprint recognition system by use of graph matching

Science.gov (United States)

Shen, Wei; Shen, Jun; Zheng, Huicheng

2001-09-01

Fingerprint recognition is an important subject in biometrics to identify or verify persons by physiological characteristics, and has found wide applications in different domains. In the present paper, we present a finger recognition system that combines singular points and structures. The principal steps of processing in our system are: preprocessing and ridge segmentation, singular point extraction and selection, graph representation, and finger recognition by graphs matching. Our fingerprint recognition system is implemented and tested for many fingerprint images and the experimental result are satisfactory. Different techniques are used in our system, such as fast calculation of orientation field, local fuzzy dynamical thresholding, algebraic analysis of connections and fingerprints representation and matching by graphs. Wed find that for fingerprint database that is not very large, the recognition rate is very high even without using a prior coarse category classification. This system works well for both one-to-few and one-to-many problems.

Cost-Sensitive Learning for Emotion Robust Speaker Recognition

Directory of Open Access Journals (Sweden)

Dongdong Li

2014-01-01

Full Text Available In the field of information security, voice is one of the most important parts in biometrics. Especially, with the development of voice communication through the Internet or telephone system, huge voice data resources are accessed. In speaker recognition, voiceprint can be applied as the unique password for the user to prove his/her identity. However, speech with various emotions can cause an unacceptably high error rate and aggravate the performance of speaker recognition system. This paper deals with this problem by introducing a cost-sensitive learning technology to reweight the probability of test affective utterances in the pitch envelop level, which can enhance the robustness in emotion-dependent speaker recognition effectively. Based on that technology, a new architecture of recognition system as well as its components is proposed in this paper. The experiment conducted on the Mandarin Affective Speech Corpus shows that an improvement of 8% identification rate over the traditional speaker recognition is achieved.

Cost-sensitive learning for emotion robust speaker recognition.

Science.gov (United States)

Li, Dongdong; Yang, Yingchun; Dai, Weihui

2014-01-01

In the field of information security, voice is one of the most important parts in biometrics. Especially, with the development of voice communication through the Internet or telephone system, huge voice data resources are accessed. In speaker recognition, voiceprint can be applied as the unique password for the user to prove his/her identity. However, speech with various emotions can cause an unacceptably high error rate and aggravate the performance of speaker recognition system. This paper deals with this problem by introducing a cost-sensitive learning technology to reweight the probability of test affective utterances in the pitch envelop level, which can enhance the robustness in emotion-dependent speaker recognition effectively. Based on that technology, a new architecture of recognition system as well as its components is proposed in this paper. The experiment conducted on the Mandarin Affective Speech Corpus shows that an improvement of 8% identification rate over the traditional speaker recognition is achieved.

Stein and Honneth on Empathy and Emotional Recognition

DEFF Research Database (Denmark)

Jardine, James Alexander

2015-01-01

My aim in this paper is to make use of Edith Stein’s phenomenological analyses of empathy, emotion, and personhood to clarify and critically assess the recent suggestion by Axel Honneth that a basic form of recognition is affective in nature. I will begin by considering Honneth’s own presentation...... of this claim in his discussion of the role of affect in recognitive gestures, as well as in his notion of ‘elementary recognition,’ arguing that while his account contains much of value it also generates problems. On the basis of this analysis, I will try to show that Stein’s account of empathy demarcates...... an elementary form of recognition in a less problematic fashion than does Honneth’s own treatment of this issue. I will then spell out the consequences of this move for the emotional recognition thesis, arguing that Stein’s treatment lends it further credence, before ending with some remarks on the connection...

Impaired face recognition is associated with social inhibition.

Science.gov (United States)

Avery, Suzanne N; VanDerKlok, Ross M; Heckers, Stephan; Blackford, Jennifer U

2016-02-28

Face recognition is fundamental to successful social interaction. Individuals with deficits in face recognition are likely to have social functioning impairments that may lead to heightened risk for social anxiety. A critical component of social interaction is how quickly a face is learned during initial exposure to a new individual. Here, we used a novel Repeated Faces task to assess how quickly memory for faces is established. Face recognition was measured over multiple exposures in 52 young adults ranging from low to high in social inhibition, a core dimension of social anxiety. High social inhibition was associated with a smaller slope of change in recognition memory over repeated face exposure, indicating participants with higher social inhibition showed smaller improvements in recognition memory after seeing faces multiple times. We propose that impaired face learning is an important mechanism underlying social inhibition and may contribute to, or maintain, social anxiety. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Biologically inspired emotion recognition from speech

Directory of Open Access Journals (Sweden)

Buscicchio Cosimo

2011-01-01

Full Text Available Abstract Emotion recognition has become a fundamental task in human-computer interaction systems. In this article, we propose an emotion recognition approach based on biologically inspired methods. Specifically, emotion classification is performed using a long short-term memory (LSTM recurrent neural network which is able to recognize long-range dependencies between successive temporal patterns. We propose to represent data using features derived from two different models: mel-frequency cepstral coefficients (MFCC and the Lyon cochlear model. In the experimental phase, results obtained from the LSTM network and the two different feature sets are compared, showing that features derived from the Lyon cochlear model give better recognition results in comparison with those obtained with the traditional MFCC representation.

Neural circuitry for rat recognition memory

Science.gov (United States)

Warburton, E.C.; Brown, M.W.

2015-01-01

Information concerning the roles of different brain regions in recognition memory processes is reviewed. The review concentrates on findings from spontaneous recognition memory tasks performed by rats, including memory for single objects, locations, object–location associations and temporal order. Particular emphasis is given to the potential roles of different regions in the circuit of interacting structures involving the perirhinal cortex, hippocampus, medial prefrontal cortex and medial dorsal thalamus in recognition memory for the association of objects and places. It is concluded that while all structures in this circuit play roles critical to such memory, these roles can potentially be differentiated and differences in the underlying synaptic and biochemical processes involved in each region are beginning to be uncovered. PMID:25315129

The effects of age and divided attention on spontaneous recognition.

Science.gov (United States)

Anderson, Benjamin A; Jacoby, Larry L; Thomas, Ruthann C; Balota, David A

2011-05-01

Studies of recognition typically involve tests in which the participant's memory for a stimulus is directly questioned. There are occasions however, in which memory occurs more spontaneously (e.g., an acquaintance seeming familiar out of context). Spontaneous recognition was investigated in a novel paradigm involving study of pictures and words followed by recognition judgments on stimuli with an old or new word superimposed over an old or new picture. Participants were instructed to make their recognition decision on either the picture or word and to ignore the distracting stimulus. Spontaneous recognition was measured as the influence of old vs. new distracters on target recognition. Across two experiments, older adults and younger adults placed under divided-attention showed a greater tendency to spontaneously recognize old distracters as compared to full-attention younger adults. The occurrence of spontaneous recognition is discussed in relation to ability to constrain retrieval to goal-relevant information.

Garbage Modeling for On-device Speech Recognition

NARCIS (Netherlands)

Van Gysel, C.; Velikovich, L.; McGraw, I.; Beaufays, F.

2015-01-01

User interactions with mobile devices increasingly depend on voice as a primary input modality. Due to the disadvantages of sending audio across potentially spotty network connections for speech recognition, in recent years there has been growing attention to performing recognition on-device. The

Iris recognition based on robust principal component analysis

Science.gov (United States)

Karn, Pradeep; He, Xiao Hai; Yang, Shuai; Wu, Xiao Hong

2014-11-01

Iris images acquired under different conditions often suffer from blur, occlusion due to eyelids and eyelashes, specular reflection, and other artifacts. Existing iris recognition systems do not perform well on these types of images. To overcome these problems, we propose an iris recognition method based on robust principal component analysis. The proposed method decomposes all training images into a low-rank matrix and a sparse error matrix, where the low-rank matrix is used for feature extraction. The sparsity concentration index approach is then applied to validate the recognition result. Experimental results using CASIA V4 and IIT Delhi V1iris image databases showed that the proposed method achieved competitive performances in both recognition accuracy and computational efficiency.

Setting a world record in 3D face recognition

NARCIS (Netherlands)

Spreeuwers, Lieuwe Jan

2015-01-01

Biometrics - recognition of persons based on how they look or behave, is the main subject of research at the Chair of Biometric Pattern Recognition (BPR) of the Services, Cyber Security and Safety Group (SCS) of the EEMCS Faculty at the University of Twente. Examples are finger print recognition,

Facial expression recognition based on improved deep belief networks

Science.gov (United States)

Wu, Yao; Qiu, Weigen

2017-08-01

In order to improve the robustness of facial expression recognition, a method of face expression recognition based on Local Binary Pattern (LBP) combined with improved deep belief networks (DBNs) is proposed. This method uses LBP to extract the feature, and then uses the improved deep belief networks as the detector and classifier to extract the LBP feature. The combination of LBP and improved deep belief networks is realized in facial expression recognition. In the JAFFE (Japanese Female Facial Expression) database on the recognition rate has improved significantly.

Local Feature Learning for Face Recognition under Varying Poses

DEFF Research Database (Denmark)

Duan, Xiaodong; Tan, Zheng-Hua

2015-01-01

In this paper, we present a local feature learning method for face recognition to deal with varying poses. As opposed to the commonly used approaches of recovering frontal face images from profile views, the proposed method extracts the subject related part from a local feature by removing the pose...... related part in it on the basis of a pose feature. The method has a closed-form solution, hence being time efficient. For performance evaluation, cross pose face recognition experiments are conducted on two public face recognition databases FERET and FEI. The proposed method shows a significant...... recognition improvement under varying poses over general local feature approaches and outperforms or is comparable with related state-of-the-art pose invariant face recognition approaches. Copyright ©2015 by IEEE....

Automatic anatomy recognition via multiobject oriented active shape models.

Science.gov (United States)

Chen, Xinjian; Udupa, Jayaram K; Alavi, Abass; Torigian, Drew A

2010-12-01

This paper studies the feasibility of developing an automatic anatomy recognition (AAR) system in clinical radiology and demonstrates its operation on clinical 2D images. The anatomy recognition method described here consists of two main components: (a) multiobject generalization of OASM and (b) object recognition strategies. The OASM algorithm is generalized to multiple objects by including a model for each object and assigning a cost structure specific to each object in the spirit of live wire. The delineation of multiobject boundaries is done in MOASM via a three level dynamic programming algorithm, wherein the first level is at pixel level which aims to find optimal oriented boundary segments between successive landmarks, the second level is at landmark level which aims to find optimal location for the landmarks, and the third level is at the object level which aims to find optimal arrangement of object boundaries over all objects. The object recognition strategy attempts to find that pose vector (consisting of translation, rotation, and scale component) for the multiobject model that yields the smallest total boundary cost for all objects. The delineation and recognition accuracies were evaluated separately utilizing routine clinical chest CT, abdominal CT, and foot MRI data sets. The delineation accuracy was evaluated in terms of true and false positive volume fractions (TPVF and FPVF). The recognition accuracy was assessed (1) in terms of the size of the space of the pose vectors for the model assembly that yielded high delineation accuracy, (2) as a function of the number of objects and objects' distribution and size in the model, (3) in terms of the interdependence between delineation and recognition, and (4) in terms of the closeness of the optimum recognition result to the global optimum. When multiple objects are included in the model, the delineation accuracy in terms of TPVF can be improved to 97%-98% with a low FPVF of 0.1%-0.2%. Typically, a

The cingulo-opercular network provides word-recognition benefit.

Science.gov (United States)

Vaden, Kenneth I; Kuchinsky, Stefanie E; Cute, Stephanie L; Ahlstrom, Jayne B; Dubno, Judy R; Eckert, Mark A

2013-11-27

Recognizing speech in difficult listening conditions requires considerable focus of attention that is often demonstrated by elevated activity in putative attention systems, including the cingulo-opercular network. We tested the prediction that elevated cingulo-opercular activity provides word-recognition benefit on a subsequent trial. Eighteen healthy, normal-hearing adults (10 females; aged 20-38 years) performed word recognition (120 trials) in multi-talker babble at +3 and +10 dB signal-to-noise ratios during a sparse sampling functional magnetic resonance imaging (fMRI) experiment. Blood oxygen level-dependent (BOLD) contrast was elevated in the anterior cingulate cortex, anterior insula, and frontal operculum in response to poorer speech intelligibility and response errors. These brain regions exhibited significantly greater correlated activity during word recognition compared with rest, supporting the premise that word-recognition demands increased the coherence of cingulo-opercular network activity. Consistent with an adaptive control network explanation, general linear mixed model analyses demonstrated that increased magnitude and extent of cingulo-opercular network activity was significantly associated with correct word recognition on subsequent trials. These results indicate that elevated cingulo-opercular network activity is not simply a reflection of poor performance or error but also supports word recognition in difficult listening conditions.

Molecular recognition by gold, silver and copper nanoparticles

Science.gov (United States)

Tauran, Yannick; Brioude, Arnaud; Coleman, Anthony W; Rhimi, Moez; Kim, Beonjoom

2013-01-01

The intrinsic physical properties of the noble metal nanoparticles, which are highly sensitive to the nature of their local molecular environment, make such systems ideal for the detection of molecular recognition events. The current review describes the state of the art concerning molecular recognition of Noble metal nanoparticles. In the first part the preparation of such nanoparticles is discussed along with methods of capping and stabilization. A brief discussion of the three common methods of functionalization: Electrostatic adsorption; Chemisorption; Affinity-based coordination is given. In the second section a discussion of the optical and electrical properties of nanoparticles is given to aid the reader in understanding the use of such properties in molecular recognition. In the main section the various types of capping agents for molecular recognition; nucleic acid coatings, protein coatings and molecules from the family of supramolecular chemistry are described along with their numerous applications. Emphasis for the nucleic acids is on complementary oligonucleotide and aptamer recognition. For the proteins the recognition properties of antibodies form the core of the section. With respect to the supramolecular systems the cyclodextrins, calix[n]arenes, dendrimers, crown ethers and the cucurbitales are treated in depth. Finally a short section deals with the possible toxicity of the nanoparticles, a concern in public health. PMID:23977421

Assessment of Self-Recognition in Young Children with Handicaps.

Science.gov (United States)

Kelley, Michael F.; And Others

1988-01-01

Thirty young children with handicaps were assessed on five self-recognition mirror tasks. The set of tasks formed a reproducible scale, indicating that these tasks are an appropriate measure of self-recognition in this population. Data analysis suggested that stage of self-recognition is positively and significantly related to cognitive…

Recognition and Accountability: Sole Parent Postgraduates in University Conditions

Science.gov (United States)

Hook, Genine A.

2015-01-01

This paper aims to examine some of ways sole parents sought recognition as postgraduate students in Australian universities. Judith Butler's theory of recognition notes that recognition is always partial and any account we give of ourselves must be given to another. Participants articulated that supervisors were critical in the process of…

Recognition of Face and Emotional Facial Expressions in Autism

Directory of Open Access Journals (Sweden)

Muhammed Tayyib Kadak

2013-03-01

Full Text Available Autism is a genetically transferred neurodevelopmental disorder characterized by severe and permanent deficits in many interpersonal relation areas like communication, social interaction and emotional responsiveness. Patients with autism have deficits in face recognition, eye contact and recognition of emotional expression. Both recognition of face and expression of facial emotion carried on face processing. Structural and functional impairment in fusiform gyrus, amygdala, superior temporal sulcus and other brain regions lead to deficits in recognition of face and facial emotion. Therefore studies suggest that face processing deficits resulted in problems in areas of social interaction and emotion in autism. Studies revealed that children with autism had problems in recognition of facial expression and used mouth region more than eye region. It was also shown that autistic patients interpreted ambiguous expressions as negative emotion. In autism, deficits related in various stages of face processing like detection of gaze, face identity, recognition of emotional expression were determined, so far. Social interaction impairments in autistic spectrum disorders originated from face processing deficits during the periods of infancy, childhood and adolescence. Recognition of face and expression of facial emotion could be affected either automatically by orienting towards faces after birth, or by “learning” processes in developmental periods such as identity and emotion processing. This article aimed to review neurobiological basis of face processing and recognition of emotional facial expressions during normal development and in autism.

Analysis of Feature Extraction Methods for Speaker Dependent Speech Recognition

Directory of Open Access Journals (Sweden)

Gurpreet Kaur

2017-02-01

Full Text Available Speech recognition is about what is being said, irrespective of who is saying. Speech recognition is a growing field. Major progress is taking place on the technology of automatic speech recognition (ASR. Still, there are lots of barriers in this field in terms of recognition rate, background noise, speaker variability, speaking rate, accent etc. Speech recognition rate mainly depends on the selection of features and feature extraction methods. This paper outlines the feature extraction techniques for speaker dependent speech recognition for isolated words. A brief survey of different feature extraction techniques like Mel-Frequency Cepstral Coefficients (MFCC, Linear Predictive Coding Coefficients (LPCC, Perceptual Linear Prediction (PLP, Relative Spectra Perceptual linear Predictive (RASTA-PLP analysis are presented and evaluation is done. Speech recognition has various applications from daily use to commercial use. We have made a speaker dependent system and this system can be useful in many areas like controlling a patient vehicle using simple commands.

Designing of Medium-Size Humanoid Robot with Face Recognition Features

Directory of Open Access Journals (Sweden)

Christian Tarunajaya

2016-02-01

Full Text Available owadays, there have been so many development of robot that can receive command and do speech recognition and face recognition. In this research, we develop a humanoid robot system with a controller that based on Raspberry Pi 2. The methods we used are based on Audio recognition and detection, and also face recognition using PCA (Principal Component Analysis with OpenCV and Python. PCA is one of the algorithms to do face detection by doing reduction to the number of dimension of the image possessed. The result of this reduction process is then known as eigenface to do face recognition process. In this research, we still find a false recognition. It can be caused by many things, like database condition, maybe the images are too dark or less varied, blur test image, etc. The accuracy from 3 tests on different people is about 93% (28 correct recognitions out of 30.

Visual word recognition across the adult lifespan.

Science.gov (United States)

Cohen-Shikora, Emily R; Balota, David A

2016-08-01

The current study examines visual word recognition in a large sample (N = 148) across the adult life span and across a large set of stimuli (N = 1,187) in three different lexical processing tasks (pronunciation, lexical decision, and animacy judgment). Although the focus of the present study is on the influence of word frequency, a diverse set of other variables are examined as the word recognition system ages and acquires more experience with language. Computational models and conceptual theories of visual word recognition and aging make differing predictions for age-related changes in the system. However, these have been difficult to assess because prior studies have produced inconsistent results, possibly because of sample differences, analytic procedures, and/or task-specific processes. The current study confronts these potential differences by using 3 different tasks, treating age and word variables as continuous, and exploring the influence of individual differences such as vocabulary, vision, and working memory. The primary finding is remarkable stability in the influence of a diverse set of variables on visual word recognition across the adult age spectrum. This pattern is discussed in reference to previous inconsistent findings in the literature and implications for current models of visual word recognition. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Multi-font printed Mongolian document recognition system

Science.gov (United States)

Peng, Liangrui; Liu, Changsong; Ding, Xiaoqing; Wang, Hua; Jin, Jianming

2009-01-01

Mongolian is one of the major ethnic languages in China. Large amount of Mongolian printed documents need to be digitized in digital library and various applications. Traditional Mongolian script has unique writing style and multi-font-type variations, which bring challenges to Mongolian OCR research. As traditional Mongolian script has some characteristics, for example, one character may be part of another character, we define the character set for recognition according to the segmented components, and the components are combined into characters by rule-based post-processing module. For character recognition, a method based on visual directional feature and multi-level classifiers is presented. For character segmentation, a scheme is used to find the segmentation point by analyzing the properties of projection and connected components. As Mongolian has different font-types which are categorized into two major groups, the parameter of segmentation is adjusted for each group. A font-type classification method for the two font-type group is introduced. For recognition of Mongolian text mixed with Chinese and English, language identification and relevant character recognition kernels are integrated. Experiments show that the presented methods are effective. The text recognition rate is 96.9% on the test samples from practical documents with multi-font-types and mixed scripts.

Incremental Learning for Place Recognition in Dynamic Environments

OpenAIRE

Luo, Jie; Pronobis, Andrzej; Caputo, Barbara; Jensfelt, Patric

2007-01-01

This paper proposes a discriminative approach to template-based Vision-based place recognition is a desirable feature for an autonomous mobile system. In order to work in realistic scenarios, visual recognition algorithms should be adaptive, i.e. should be able to learn from experience and adapt continuously to changes in the environment. This paper presents a discriminative incremental learning approach to place recognition. We use a recently introduced version of the incremental SVM, which ...

When is the hippocampus involved in recognition memory?

OpenAIRE

Barker, Gareth R. I.; Warburton, Elizabeth C.

2011-01-01

The role of the hippocampus in recognition memory is controversial. Recognition memory judgments may be made using different types of information, including object familiarity, an object's spatial location, or when an object was encountered. Experiment 1 examined the role of the hippocampus in recognition memory tasks that required the animals to use these different types of mnemonic information. Rats with bilateral cytotoxic lesions in the hippocampus or perirhinal or prefrontal cortex were ...

Social appraisal influences recognition of emotions.

Science.gov (United States)

Mumenthaler, Christian; Sander, David

2012-06-01

The notion of social appraisal emphasizes the importance of a social dimension in appraisal theories of emotion by proposing that the way an individual appraises an event is influenced by the way other individuals appraise and feel about the same event. This study directly tested this proposal by asking participants to recognize dynamic facial expressions of emotion (fear, happiness, or anger in Experiment 1; fear, happiness, anger, or neutral in Experiment 2) in a target face presented at the center of a screen while a contextual face, which appeared simultaneously in the periphery of the screen, expressed an emotion (fear, happiness, anger) or not (neutral) and either looked at the target face or not. We manipulated gaze direction to be able to distinguish between a mere contextual effect (gaze away from both the target face and the participant) and a specific social appraisal effect (gaze toward the target face). Results of both experiments provided evidence for a social appraisal effect in emotion recognition, which differed from the mere effect of contextual information: Whereas facial expressions were identical in both conditions, the direction of the gaze of the contextual face influenced emotion recognition. Social appraisal facilitated the recognition of anger, happiness, and fear when the contextual face expressed the same emotion. This facilitation was stronger than the mere contextual effect. Social appraisal also allowed better recognition of fear when the contextual face expressed anger and better recognition of anger when the contextual face expressed fear. 2012 APA, all rights reserved

Deep kernel learning method for SAR image target recognition

Science.gov (United States)

Chen, Xiuyuan; Peng, Xiyuan; Duan, Ran; Li, Junbao

2017-10-01

With the development of deep learning, research on image target recognition has made great progress in recent years. Remote sensing detection urgently requires target recognition for military, geographic, and other scientific research. This paper aims to solve the synthetic aperture radar image target recognition problem by combining deep and kernel learning. The model, which has a multilayer multiple kernel structure, is optimized layer by layer with the parameters of Support Vector Machine and a gradient descent algorithm. This new deep kernel learning method improves accuracy and achieves competitive recognition results compared with other learning methods.

Impaired Facial Expression Recognition in Children with Temporal Lobe Epilepsy: Impact of Early Seizure Onset on Fear Recognition

Science.gov (United States)

Golouboff, Nathalie; Fiori, Nicole; Delalande, Olivier; Fohlen, Martine; Dellatolas, Georges; Jambaque, Isabelle

2008-01-01

The amygdala has been implicated in the recognition of facial emotions, especially fearful expressions, in adults with early-onset right temporal lobe epilepsy (TLE). The present study investigates the recognition of facial emotions in children and adolescents, 8-16 years old, with epilepsy. Twenty-nine subjects had TLE (13 right, 16 left) and…

Individual recognition based on communication behaviour of male fowl.

Science.gov (United States)

Smith, Carolynn L; Taubert, Jessica; Weldon, Kimberly; Evans, Christopher S

2016-04-01

Correctly directing social behaviour towards a specific individual requires an ability to discriminate between conspecifics. The mechanisms of individual recognition include phenotype matching and familiarity-based recognition. Communication-based recognition is a subset of familiarity-based recognition wherein the classification is based on behavioural or distinctive signalling properties. Male fowl (Gallus gallus) produce a visual display (tidbitting) upon finding food in the presence of a female. Females typically approach displaying males. However, males may tidbit without food. We used the distinctiveness of the visual display and the unreliability of some males to test for communication-based recognition in female fowl. We manipulated the prior experience of the hens with the males to create two classes of males: S(+) wherein the tidbitting signal was paired with a food reward to the female, and S (-) wherein the tidbitting signal occurred without food reward. We then conducted a sequential discrimination test with hens using a live video feed of a familiar male. The results of the discrimination tests revealed that hens discriminated between categories of males based on their signalling behaviour. These results suggest that fowl possess a communication-based recognition system. This is the first demonstration of live-to-video transfer of recognition in any species of bird. Copyright © 2016 Elsevier B.V. All rights reserved.

Confidence in Forced-Choice Recognition: What Underlies the Ratings?

Science.gov (United States)

Zawadzka, Katarzyna; Higham, Philip A.; Hanczakowski, Maciej

2017-01-01

Two-alternative forced-choice recognition tests are commonly used to assess recognition accuracy that is uncontaminated by changes in bias. In such tests, participants are asked to endorse the studied item out of 2 presented alternatives. Participants may be further asked to provide confidence judgments for their recognition decisions. It is often…

Syllable Transposition Effects in Korean Word Recognition

Science.gov (United States)

Lee, Chang H.; Kwon, Youan; Kim, Kyungil; Rastle, Kathleen

2015-01-01

Research on the impact of letter transpositions in visual word recognition has yielded important clues about the nature of orthographic representations. This study investigated the impact of syllable transpositions on the recognition of Korean multisyllabic words. Results showed that rejection latencies in visual lexical decision for…

Privacy-preserving architecture for forensic image recognition

NARCIS (Netherlands)

Peter, Andreas; Hartman, T.; Muller, S.; Katzenbeisser, S.

2013-01-01

Forensic image recognition is an important tool in many areas of law enforcement where an agency wants to prosecute possessors of illegal images. The recognition of illegal images that might have undergone human imperceptible changes (e.g., a JPEG-recompression) is commonly done by computing a

View based approach to forensic face recognition

NARCIS (Netherlands)

Dutta, A.; van Rootseler, R.T.A.; Veldhuis, Raymond N.J.; Spreeuwers, Lieuwe Jan

Face recognition is a challenging problem for surveillance view images commonly encountered in a forensic face recognition case. One approach to deal with a non-frontal test image is to synthesize the corresponding frontal view image and compare it with frontal view reference images. However, it is

Pattern recognition and modelling of earthquake registrations with interactive computer support

International Nuclear Information System (INIS)

Manova, Katarina S.

2004-01-01

The object of the thesis is Pattern Recognition. Pattern recognition i.e. classification, is applied in many fields: speech recognition, hand printed character recognition, medical analysis, satellite and aerial-photo interpretations, biology, computer vision, information retrieval and so on. In this thesis is studied its applicability in seismology. Signal classification is an area of great importance in a wide variety of applications. This thesis deals with the problem of (automatic) classification of earthquake signals, which are non-stationary signals. Non-stationary signal classification is an area of active research in the signal and image processing community. The goal of the thesis is recognition of earthquake signals according to their epicentral zone. Source classification i.e. recognition is based on transformation of seismograms (earthquake registrations) to images, via time-frequency transformations, and applying image processing and pattern recognition techniques for feature extraction, classification and recognition. The tested data include local earthquakes from seismic regions in Macedonia. By using actual seismic data it is shown that proposed methods provide satisfactory results for classification and recognition.(Author)

Pattern Recognition Control Design

Science.gov (United States)

Gambone, Elisabeth A.

2018-01-01

Spacecraft control algorithms must know the expected vehicle response to any command to the available control effectors, such as reaction thrusters or torque devices. Spacecraft control system design approaches have traditionally relied on the estimated vehicle mass properties to determine the desired force and moment, as well as knowledge of the effector performance to efficiently control the spacecraft. A pattern recognition approach was used to investigate the relationship between the control effector commands and spacecraft responses. Instead of supplying the approximated vehicle properties and the thruster performance characteristics, a database of information relating the thruster ring commands and the desired vehicle response was used for closed-loop control. A Monte Carlo simulation data set of the spacecraft dynamic response to effector commands was analyzed to establish the influence a command has on the behavior of the spacecraft. A tool developed at NASA Johnson Space Center to analyze flight dynamics Monte Carlo data sets through pattern recognition methods was used to perform this analysis. Once a comprehensive data set relating spacecraft responses with commands was established, it was used in place of traditional control methods and gains set. This pattern recognition approach was compared with traditional control algorithms to determine the potential benefits and uses.

Acute effects of triazolam on false recognition.

Science.gov (United States)

Mintzer, M Z; Griffiths, R R

2000-12-01

Neuropsychological, neuroimaging, and electrophysiological techniques have been applied to the study of false recognition; however, psychopharmacological techniques have not been applied. Benzodiazepine sedative/anxiolytic drugs produce memory deficits similar to those observed in organic amnesia and may be useful tools for studying normal and abnormal memory mechanisms. The present double-blind, placebo-controlled repeated measures study examined the acute effects of orally administered triazolam (Halcion; 0.125 and 0.25 mg/70 kg), a benzodiazepine hypnotic, on performance in the Deese (1959)/Roediger-McDermott (1995) false recognition paradigm in 24 healthy volunteers. Paralleling previous demonstrations in amnesic patients, triazolam produced significant dose-related reductions in false recognition rates to nonstudied words associatively related to studied words, suggesting that false recognition relies on normal memory mechanisms impaired in benzodiazepine-induced amnesia. The results also suggested that relative to placebo, triazolam reduced participants' reliance on memory for item-specific versus list-common semantic information and reduced participants' use of remember versus know responses.

The neural correlates of visual self-recognition.

Science.gov (United States)

Devue, Christel; Brédart, Serge

2011-03-01

This paper presents a review of studies that were aimed at determining which brain regions are recruited during visual self-recognition, with a particular focus on self-face recognition. A complex bilateral network, involving frontal, parietal and occipital areas, appears to be associated with self-face recognition, with a particularly high implication of the right hemisphere. Results indicate that it remains difficult to determine which specific cognitive operation is reflected by each recruited brain area, in part due to the variability of used control stimuli and experimental tasks. A synthesis of the interpretations provided by previous studies is presented. The relevance of using self-recognition as an indicator of self-awareness is discussed. We argue that a major aim of future research in the field should be to identify more clearly the cognitive operations induced by the perception of the self-face, and search for dissociations between neural correlates and cognitive components. Copyright © 2010 Elsevier Inc. All rights reserved.

Compact Acoustic Models for Embedded Speech Recognition

Directory of Open Access Journals (Sweden)

Lévy Christophe

2009-01-01

Full Text Available Speech recognition applications are known to require a significant amount of resources. However, embedded speech recognition only authorizes few KB of memory, few MIPS, and small amount of training data. In order to fit the resource constraints of embedded applications, an approach based on a semicontinuous HMM system using state-independent acoustic modelling is proposed. A transformation is computed and applied to the global model in order to obtain each HMM state-dependent probability density functions, authorizing to store only the transformation parameters. This approach is evaluated on two tasks: digit and voice-command recognition. A fast adaptation technique of acoustic models is also proposed. In order to significantly reduce computational costs, the adaptation is performed only on the global model (using related speaker recognition adaptation techniques with no need for state-dependent data. The whole approach results in a relative gain of more than 20% compared to a basic HMM-based system fitting the constraints.

Novel Techniques for Dialectal Arabic Speech Recognition

CERN Document Server

Elmahdy, Mohamed; Minker, Wolfgang

2012-01-01

Novel Techniques for Dialectal Arabic Speech describes approaches to improve automatic speech recognition for dialectal Arabic. Since speech resources for dialectal Arabic speech recognition are very sparse, the authors describe how existing Modern Standard Arabic (MSA) speech data can be applied to dialectal Arabic speech recognition, while assuming that MSA is always a second language for all Arabic speakers. In this book, Egyptian Colloquial Arabic (ECA) has been chosen as a typical Arabic dialect. ECA is the first ranked Arabic dialect in terms of number of speakers, and a high quality ECA speech corpus with accurate phonetic transcription has been collected. MSA acoustic models were trained using news broadcast speech. In order to cross-lingually use MSA in dialectal Arabic speech recognition, the authors have normalized the phoneme sets for MSA and ECA. After this normalization, they have applied state-of-the-art acoustic model adaptation techniques like Maximum Likelihood Linear Regression (MLLR) and M...

Food-Induced Emotional Resonance Improves Emotion Recognition.

Science.gov (United States)

Pandolfi, Elisa; Sacripante, Riccardo; Cardini, Flavia

2016-01-01

The effect of food substances on emotional states has been widely investigated, showing, for example, that eating chocolate is able to reduce negative mood. Here, for the first time, we have shown that the consumption of specific food substances is not only able to induce particular emotional states, but more importantly, to facilitate recognition of corresponding emotional facial expressions in others. Participants were asked to perform an emotion recognition task before and after eating either a piece of chocolate or a small amount of fish sauce-which we expected to induce happiness or disgust, respectively. Our results showed that being in a specific emotional state improves recognition of the corresponding emotional facial expression. Indeed, eating chocolate improved recognition of happy faces, while disgusted expressions were more readily recognized after eating fish sauce. In line with the embodied account of emotion understanding, we suggest that people are better at inferring the emotional state of others when their own emotional state resonates with the observed one.

Food-Induced Emotional Resonance Improves Emotion Recognition

Science.gov (United States)

Pandolfi, Elisa; Sacripante, Riccardo; Cardini, Flavia

2016-01-01

The effect of food substances on emotional states has been widely investigated, showing, for example, that eating chocolate is able to reduce negative mood. Here, for the first time, we have shown that the consumption of specific food substances is not only able to induce particular emotional states, but more importantly, to facilitate recognition of corresponding emotional facial expressions in others. Participants were asked to perform an emotion recognition task before and after eating either a piece of chocolate or a small amount of fish sauce—which we expected to induce happiness or disgust, respectively. Our results showed that being in a specific emotional state improves recognition of the corresponding emotional facial expression. Indeed, eating chocolate improved recognition of happy faces, while disgusted expressions were more readily recognized after eating fish sauce. In line with the embodied account of emotion understanding, we suggest that people are better at inferring the emotional state of others when their own emotional state resonates with the observed one. PMID:27973559

Institutional Choice and Recognition in Development

DEFF Research Database (Denmark)

Rutt, Rebecca Leigh

Abstract This thesis concerns the role of local institutions in fostering development including natural resource management, and how this role is shaped by relations with higher scale institutions such as development agencies and national governments. Specifically, it examines the choice of local...... objective of this thesis was to contribute to understanding processes and outcomes of institutional choice and recognition. It employed mixed methods but primarily semi structured interviews in multiple sites across Nepal. In responding to specific objectives, namely to better understand: i) the rationales...... behind choices of local institutional counterparts, ii) the belonging and citizenship available with local institutions, iii) the dynamics and mutuality of recognition between higher and lower scale institutions, and iv) the social outcomes of choice and recognition, this thesis shows that the way choice...

Acoustic modeling for emotion recognition

CERN Document Server

Anne, Koteswara Rao; Vankayalapati, Hima Deepthi

2015-01-01

 This book presents state of art research in speech emotion recognition. Readers are first presented with basic research and applications – gradually more advance information is provided, giving readers comprehensive guidance for classify emotions through speech. Simulated databases are used and results extensively compared, with the features and the algorithms implemented using MATLAB. Various emotion recognition models like Linear Discriminant Analysis (LDA), Regularized Discriminant Analysis (RDA), Support Vector Machines (SVM) and K-Nearest neighbor (KNN) and are explored in detail using prosody and spectral features, and feature fusion techniques.

Object recognition with hierarchical discriminant saliency networks.

Science.gov (United States)

Han, Sunhyoung; Vasconcelos, Nuno

2014-01-01

The benefits of integrating attention and object recognition are investigated. While attention is frequently modeled as a pre-processor for recognition, we investigate the hypothesis that attention is an intrinsic component of recognition and vice-versa. This hypothesis is tested with a recognition model, the hierarchical discriminant saliency network (HDSN), whose layers are top-down saliency detectors, tuned for a visual class according to the principles of discriminant saliency. As a model of neural computation, the HDSN has two possible implementations. In a biologically plausible implementation, all layers comply with the standard neurophysiological model of visual cortex, with sub-layers of simple and complex units that implement a combination of filtering, divisive normalization, pooling, and non-linearities. In a convolutional neural network implementation, all layers are convolutional and implement a combination of filtering, rectification, and pooling. The rectification is performed with a parametric extension of the now popular rectified linear units (ReLUs), whose parameters can be tuned for the detection of target object classes. This enables a number of functional enhancements over neural network models that lack a connection to saliency, including optimal feature denoising mechanisms for recognition, modulation of saliency responses by the discriminant power of the underlying features, and the ability to detect both feature presence and absence. In either implementation, each layer has a precise statistical interpretation, and all parameters are tuned by statistical learning. Each saliency detection layer learns more discriminant saliency templates than its predecessors and higher layers have larger pooling fields. This enables the HDSN to simultaneously achieve high selectivity to target object classes and invariance. The performance of the network in saliency and object recognition tasks is compared to those of models from the biological and

Automatic Number Plate Recognition System for IPhone Devices

Directory of Open Access Journals (Sweden)

Călin Enăchescu

2013-06-01

Full Text Available This paper presents a system for automatic number plate recognition, implemented for devices running the iOS operating system. The methods used for number plate recognition are based on existing methods, but optimized for devices with low hardware resources. To solve the task of automatic number plate recognition we have divided it into the following subtasks: image acquisition, localization of the number plate position on the image and character detection. The first subtask is performed by the camera of an iPhone, the second one is done using image pre-processing methods and template matching. For the character recognition we are using a feed-forward artificial neural network. Each of these methods is presented along with its results.

A Survey on Sentiment Classification in Face Recognition

Science.gov (United States)

Qian, Jingyu

2018-01-01

Face recognition has been an important topic for both industry and academia for a long time. K-means clustering, autoencoder, and convolutional neural network, each representing a design idea for face recognition method, are three popular algorithms to deal with face recognition problems. It is worthwhile to summarize and compare these three different algorithms. This paper will focus on one specific face recognition problem-sentiment classification from images. Three different algorithms for sentiment classification problems will be summarized, including k-means clustering, autoencoder, and convolutional neural network. An experiment with the application of these algorithms on a specific dataset of human faces will be conducted to illustrate how these algorithms are applied and their accuracy. Finally, the three algorithms are compared based on the accuracy result.

A Survey of Face Recognition Technique | Omidiora | Journal of ...

African Journals Online (AJOL)

A review of face recognition techniques has been carried out. Face recognition has been an attractive field in the society of both biological and computer vision of research. It exhibits the characteristics of being natural and low-intrusive. In this paper, an updated survey of techniques for face recognition is made. Methods of ...

DEVELOPMENT OF HOLE RECOGNITION SYSTEM FROM STEP FILE

Directory of Open Access Journals (Sweden)

C. F. Tan

2017-11-01

Full Text Available This paper describes the development of Hole Recognition System (HRS for Computer-Aided Process Planning (CAPP using a neutral data format produced by CAD system. The geometrical data of holes is retrieved from STandard for the Exchange of Product model data (STEP. Rule-based algorithm is used during recognising process. Current implementation of feature recognition is limited to simple hole feat ures. Test results are presented to demonstrate the capabilities of the feature recognition algorithm.

Color constancy in 3D-2D face recognition

Science.gov (United States)

Meyer, Manuel; Riess, Christian; Angelopoulou, Elli; Evangelopoulos, Georgios; Kakadiaris, Ioannis A.

2013-05-01

Face is one of the most popular biometric modalities. However, up to now, color is rarely actively used in face recognition. Yet, it is well-known that when a person recognizes a face, color cues can become as important as shape, especially when combined with the ability of people to identify the color of objects independent of illuminant color variations. In this paper, we examine the feasibility and effect of explicitly embedding illuminant color information in face recognition systems. We empirically examine the theoretical maximum gain of including known illuminant color to a 3D-2D face recognition system. We also investigate the impact of using computational color constancy methods for estimating the illuminant color, which is then incorporated into the face recognition framework. Our experiments show that under close-to-ideal illumination estimates, one can improve face recognition rates by 16%. When the illuminant color is algorithmically estimated, the improvement is approximately 5%. These results suggest that color constancy has a positive impact on face recognition, but the accuracy of the illuminant color estimate has a considerable effect on its benefits.

Regression-based Multi-View Facial Expression Recognition

NARCIS (Netherlands)

Rudovic, Ognjen; Patras, Ioannis; Pantic, Maja

2010-01-01

We present a regression-based scheme for multi-view facial expression recognition based on 2蚠D geometric features. We address the problem by mapping facial points (e.g. mouth corners) from non-frontal to frontal view where further recognition of the expressions can be performed using a

Adult Word Recognition and Visual Sequential Memory

Science.gov (United States)

Holmes, V. M.

2012-01-01

Two experiments were conducted investigating the role of visual sequential memory skill in the word recognition efficiency of undergraduate university students. Word recognition was assessed in a lexical decision task using regularly and strangely spelt words, and nonwords that were either standard orthographically legal strings or items made from…

Finger Vein Recognition Based on Personalized Weight Maps

Science.gov (United States)

Yang, Gongping; Xiao, Rongyang; Yin, Yilong; Yang, Lu

2013-01-01

Finger vein recognition is a promising biometric recognition technology, which verifies identities via the vein patterns in the fingers. Binary pattern based methods were thoroughly studied in order to cope with the difficulties of extracting the blood vessel network. However, current binary pattern based finger vein matching methods treat every bit of feature codes derived from different image of various individuals as equally important and assign the same weight value to them. In this paper, we propose a finger vein recognition method based on personalized weight maps (PWMs). The different bits have different weight values according to their stabilities in a certain number of training samples from an individual. Firstly we present the concept of PWM, and then propose the finger vein recognition framework, which mainly consists of preprocessing, feature extraction, and matching. Finally, we design extensive experiments to evaluate the effectiveness of our proposal. Experimental results show that PWM achieves not only better performance, but also high robustness and reliability. In addition, PWM can be used as a general framework for binary pattern based recognition. PMID:24025556

Recovery from emotion recognition impairment after temporal lobectomy

Directory of Open Access Journals (Sweden)

Francesca eBenuzzi

2014-06-01

Full Text Available Mesial temporal lobe epilepsy (MTLE can be associated with emotion recognition impairment that can be particularly severe in patients with early onset seizures (1-3. Whereas there is growing evidence that memory and language can improve in seizure-free patients after anterior temporal lobectomy (ATL (4, the effects of surgery on emotional processing are still unknown. We used functional magnetic resonance imaging (fMRI to investigate short-term reorganization of networks engaged in facial emotion recognition in MTLE patients. Behavioral and fMRI data were collected from six patients before and after ATL. During the fMRI scan, patients were asked to make a gender decision on fearful and neutral faces. Behavioral data demonstrated that two patients with early-onset right MTLE were impaired in fear recognition while fMRI results showed they lacked specific activations for fearful faces. Post-ATL behavioral data showed improved emotion recognition ability, while fMRI demonstrated the recruitment of a functional network for fearful face processing. Our results suggest that ATL elicited brain plasticity mechanisms allowing behavioral and fMRI improvement in emotion recognition.

Finger Vein Recognition Based on Personalized Weight Maps

Directory of Open Access Journals (Sweden)

Lu Yang

2013-09-01

Full Text Available Finger vein recognition is a promising biometric recognition technology, which verifies identities via the vein patterns in the fingers. Binary pattern based methods were thoroughly studied in order to cope with the difficulties of extracting the blood vessel network. However, current binary pattern based finger vein matching methods treat every bit of feature codes derived from different image of various individuals as equally important and assign the same weight value to them. In this paper, we propose a finger vein recognition method based on personalized weight maps (PWMs. The different bits have different weight values according to their stabilities in a certain number of training samples from an individual. Firstly we present the concept of PWM, and then propose the finger vein recognition framework, which mainly consists of preprocessing, feature extraction, and matching. Finally, we design extensive experiments to evaluate the effectiveness of our proposal. Experimental results show that PWM achieves not only better performance, but also high robustness and reliability. In addition, PWM can be used as a general framework for binary pattern based recognition.

Is emotion recognition the only problem in ADHD? effects of pharmacotherapy on face and emotion recognition in children with ADHD.

Science.gov (United States)

Demirci, Esra; Erdogan, Ayten

2016-12-01

The objectives of this study were to evaluate both face and emotion recognition, to detect differences among attention deficit and hyperactivity disorder (ADHD) subgroups, to identify effects of the gender and to assess the effects of methylphenidate and atomoxetine treatment on both face and emotion recognition in patients with ADHD. The study sample consisted of 41 male, 29 female patients, 8-15 years of age, who were diagnosed as having combined type ADHD (N = 26), hyperactive/impulsive type ADHD (N = 21) or inattentive type ADHD (N = 23) but had not previously used any medication for ADHD and 35 male, 25 female healthy individuals. Long-acting methylphenidate (OROS-MPH) was prescribed to 38 patients, whereas atomoxetine was prescribed to 32 patients. The reading the mind in the eyes test (RMET) and Benton face recognition test (BFRT) were applied to all participants before and after treatment. The patients with ADHD had a significantly lower number of correct answers in child and adolescent RMET and in BFRT than the healthy controls. Among the ADHD subtypes, the hyperactive/impulsive subtype had a lower number of correct answers in the RMET than the inattentive subtypes, and the hyperactive/impulsive subtype had a lower number of correct answers in short and long form of BFRT than the combined and inattentive subtypes. Male and female patients with ADHD did not differ significantly with respect to the number of correct answers on the RMET and BFRT. The patients showed significant improvement in RMET and BFRT after treatment with OROS-MPH or atomoxetine. Patients with ADHD have difficulties in face recognition as well as emotion recognition. Both OROS-MPH and atomoxetine affect emotion recognition. However, further studies on the face and emotion recognition are needed in ADHD.

Person Recognition in Social Media Photos

OpenAIRE

Oh, Seong Joon; Benenson, Rodrigo; Fritz, Mario; Schiele, Bernt

2017-01-01

People nowadays share large parts of their personal lives through social media. Being able to automatically recognise people in personal photos may greatly enhance user convenience by easing photo album organisation. For human identification task, however, traditional focus of computer vision has been face recognition and pedestrian re-identification. Person recognition in social media photos sets new challenges for computer vision, including non-cooperative subjects (e.g. backward viewpoints...

Recognition of oral spelling is diagnostic of the central reading processes.

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Schubert, Teresa; McCloskey, Michael

2015-01-01

The task of recognition of oral spelling (stimulus: "C-A-T", response: "cat") is often administered to individuals with acquired written language disorders, yet there is no consensus about the underlying cognitive processes. We adjudicate between two existing hypotheses: Recognition of oral spelling uses central reading processes, or recognition of oral spelling uses central spelling processes in reverse. We tested the recognition of oral spelling and spelling to dictation abilities of a single individual with acquired dyslexia and dysgraphia. She was impaired relative to matched controls in spelling to dictation but unimpaired in recognition of oral spelling. Recognition of oral spelling for exception words (e.g., colonel) and pronounceable nonwords (e.g., larth) was intact. Our results were predicted by the hypothesis that recognition of oral spelling involves the central reading processes. We conclude that recognition of oral spelling is a useful tool for probing the integrity of the central reading processes.

Automatic anatomy recognition on CT images with pathology

Science.gov (United States)

Huang, Lidong; Udupa, Jayaram K.; Tong, Yubing; Odhner, Dewey; Torigian, Drew A.

2016-03-01

Body-wide anatomy recognition on CT images with pathology becomes crucial for quantifying body-wide disease burden. This, however, is a challenging problem because various diseases result in various abnormalities of objects such as shape and intensity patterns. We previously developed an automatic anatomy recognition (AAR) system [1] whose applicability was demonstrated on near normal diagnostic CT images in different body regions on 35 organs. The aim of this paper is to investigate strategies for adapting the previous AAR system to diagnostic CT images of patients with various pathologies as a first step toward automated body-wide disease quantification. The AAR approach consists of three main steps - model building, object recognition, and object delineation. In this paper, within the broader AAR framework, we describe a new strategy for object recognition to handle abnormal images. In the model building stage an optimal threshold interval is learned from near-normal training images for each object. This threshold is optimally tuned to the pathological manifestation of the object in the test image. Recognition is performed following a hierarchical representation of the objects. Experimental results for the abdominal body region based on 50 near-normal images used for model building and 20 abnormal images used for object recognition show that object localization accuracy within 2 voxels for liver and spleen and 3 voxels for kidney can be achieved with the new strategy.

Improved pattern recognition systems by hybrid methods

International Nuclear Information System (INIS)

Duerr, B.; Haettich, W.; Tropf, H.; Winkler, G.; Fraunhofer-Gesellschaft zur Foerderung der Angewandten Forschung e.V., Karlsruhe

1978-12-01

This report describes a combination of statistical and syntactical pattern recongition methods. The hierarchically structured recognition system consists of a conventional statistical classifier, a structural classifier analysing the topological composition of the patterns, a stage reducing the number of hypotheses made by the first two stages, and a mixed stage based on a search for maximum similarity between syntactically generated prototypes and patterns. The stages work on different principles to avoid mistakes made in one stage in the other stages. This concept is applied to the recognition of numerals written without constraints. If no samples are rejected, a recognition rate of 99,5% is obtained. (orig.) [de

Object recognition memory: neurobiological mechanisms of encoding, consolidation and retrieval.

Science.gov (United States)

Winters, Boyer D; Saksida, Lisa M; Bussey, Timothy J

2008-07-01

Tests of object recognition memory, or the judgment of the prior occurrence of an object, have made substantial contributions to our understanding of the nature and neurobiological underpinnings of mammalian memory. Only in recent years, however, have researchers begun to elucidate the specific brain areas and neural processes involved in object recognition memory. The present review considers some of this recent research, with an emphasis on studies addressing the neural bases of perirhinal cortex-dependent object recognition memory processes. We first briefly discuss operational definitions of object recognition and the common behavioural tests used to measure it in non-human primates and rodents. We then consider research from the non-human primate and rat literature examining the anatomical basis of object recognition memory in the delayed nonmatching-to-sample (DNMS) and spontaneous object recognition (SOR) tasks, respectively. The results of these studies overwhelmingly favor the view that perirhinal cortex (PRh) is a critical region for object recognition memory. We then discuss the involvement of PRh in the different stages--encoding, consolidation, and retrieval--of object recognition memory. Specifically, recent work in rats has indicated that neural activity in PRh contributes to object memory encoding, consolidation, and retrieval processes. Finally, we consider the pharmacological, cellular, and molecular factors that might play a part in PRh-mediated object recognition memory. Recent studies in rodents have begun to indicate the remarkable complexity of the neural substrates underlying this seemingly simple aspect of declarative memory.

Investigations into the involvement of NMDA mechanisms in recognition memory.

Science.gov (United States)

Warburton, E Clea; Barker, Gareth R I; Brown, Malcom W

2013-11-01

This review will focus on evidence showing that NMDA receptor neurotransmission is critical for synaptic plasticity processes within brain regions known to be necessary for the formation of object recognition memories. The aim will be to provide evidence concerning NMDA mechanisms related to recognition memory processes and show that recognition memory for objects, places or associations between objects and places depends on NMDA neurotransmission within the perirhinal cortex, temporal association cortex medial prefrontal cortex and hippocampus. Administration of the NMDA antagonist AP5, selectively into each of these brain regions has revealed that the extent of the involvement NMDA receptors appears dependent on the type of information required to solve the recognition memory task; thus NMDA receptors in the perirhinal cortex are crucial for the encoding of long-term recognition memory for objects, and object-in-place associations, but not for short-term recognition memory or for retrieval. In contrast the hippocampus and medial prefrontal cortex are required for both long-term and short-term recognition memory for places or associations between objects and places, or for recognition memory tasks that have a temporal component. Such studies have therefore confirmed that the multiple brain regions make distinct contributions to recognition memory but in addition that more than one synaptic plasticity process must be involved. This article is part of the Special Issue entitled 'Glutamate Receptor-Dependent Synaptic Plasticity'. Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.

Automated Degradation Diagnosis in Character Recognition System Subject to Camera Vibration

Directory of Open Access Journals (Sweden)

Chunmei Liu

2014-01-01

Full Text Available Degradation diagnosis plays an important role for degraded character processing, which can tell the recognition difficulty of a given degraded character. In this paper, we present a framework for automated degraded character recognition system by statistical syntactic approach using 3D primitive symbol, which is integrated by degradation diagnosis to provide accurate and reliable recognition results. Our contribution is to design the framework to build the character recognition submodels corresponding to degradation subject to camera vibration or out of focus. In each character recognition submodel, statistical syntactic approach using 3D primitive symbol is proposed to improve degraded character recognition performance. In the experiments, we show attractive experimental results, highlighting the system efficiency and recognition performance by statistical syntactic approach using 3D primitive symbol on the degraded character dataset.

Schizotypy and impaired basic face recognition? Another non-confirmatory study.

Science.gov (United States)

Bell, Vaughan; Halligan, Peter

2015-12-01

Although schizotypy has been found to be reliably associated with a reduced recognition of facial affect, the few studies that have tested the association between basic face recognition abilities and schizotypy have found mixed results. This study formally tested the association in a large non-clinical sample with established neurological measures of face recognition. Two hundred and twenty-seven participants completed the Oxford-Liverpool Inventory of Feelings and Experiences schizotypy scale and completed the Famous Faces Test and the Cardiff Repeated Recognition Test for Faces. No association between any schizotypal dimension and performance on either of the facial recognition and learning tests was found. The null results can be accepted with a high degree of confidence. Further additional evidence is provided for a lack of association between schizotypy and basic face recognition deficits. © 2014 Wiley Publishing Asia Pty Ltd.

Traffic sign recognition with deep convolutional neural networks

OpenAIRE

Karamatić, Boris

2016-01-01

The problem of detection and recognition of traffic signs is becoming an important problem when it comes to the development of self driving cars and advanced driver assistance systems. In this thesis we will develop a system for detection and recognition of traffic signs. For the problem of detection we will use aggregate channel features and for the problem of recognition we will use a deep convolutional neural network. We will describe how convolutional neural networks work, how they are co...

Iris image enhancement for feature recognition and extraction

CSIR Research Space (South Africa)

Mabuza, GP

2012-10-01

Full Text Available the employment of other algorithms and commands so as to better present and demonstrate the obtained results. Edge detection and enhancing images for use in an iris recognition system allow for efficient recognition and extraction of iris patterns. REFERENCES... Gonzalez, R.C. and Woods, R.E. 2002. Digital Image Processing 2nd Edition, Instructor?s manual .Englewood Cliffs, Prentice Hall, pp 17-36. Proen?a, H. and Alexandre, L.A. 2007. Toward Noncooperative Iris Recognition: A classification approach using...

Quadcopter Control Using Speech Recognition

Science.gov (United States)

Malik, H.; Darma, S.; Soekirno, S.

2018-04-01

This research reported a comparison from a success rate of speech recognition systems that used two types of databases they were existing databases and new databases, that were implemented into quadcopter as motion control. Speech recognition system was using Mel frequency cepstral coefficient method (MFCC) as feature extraction that was trained using recursive neural network method (RNN). MFCC method was one of the feature extraction methods that most used for speech recognition. This method has a success rate of 80% - 95%. Existing database was used to measure the success rate of RNN method. The new database was created using Indonesian language and then the success rate was compared with results from an existing database. Sound input from the microphone was processed on a DSP module with MFCC method to get the characteristic values. Then, the characteristic values were trained using the RNN which result was a command. The command became a control input to the single board computer (SBC) which result was the movement of the quadcopter. On SBC, we used robot operating system (ROS) as the kernel (Operating System).

Gender recognition from vocal source

Science.gov (United States)

Sorokin, V. N.; Makarov, I. S.

2008-07-01

Efficiency of automatic recognition of male and female voices based on solving the inverse problem for glottis area dynamics and for waveform of the glottal airflow volume velocity pulse is studied. The inverse problem is regularized through the use of analytical models of the voice excitation pulse and of the dynamics of the glottis area, as well as the model of one-dimensional glottal airflow. Parameters of these models and spectral parameters of the volume velocity pulse are considered. The following parameters are found to be most promising: the instant of maximum glottis area, the maximum derivative of the area, the slope of the spectrum of the glottal airflow volume velocity pulse, the amplitude ratios of harmonics of this spectrum, and the pitch. On the plane of the first two main components in the space of these parameters, an almost twofold decrease in the classification error relative to that for the pitch alone is attained. The male voice recognition probability is found to be 94.7%, and the female voice recognition probability is 95.9%.

The diffusion of Magnet hospital recognition.

Science.gov (United States)

Abraham, Jean; Jerome-D'Emilia, Bonnie; Begun, James W

2011-01-01

Magnet recognition is promoted by many in the practice community as the gold standard of nursing care quality. The Magnet hospital population has exploded in recent years, with about 8% of U.S. general hospitals now recognized. The purpose of this study was to identify the characteristics that distinguish Magnet-recognized hospitals from other hospitals within the framework of diffusion theory. We conceptualize Magnet recognition as an organizational innovation and Magnet-recognized hospitals as adopters of the innovation. We hypothesize that adoption is associated with selected characteristics of hospitals and their markets. The study population consists of the 3,657 general hospitals in the United States in 2008 located in metropolitan or micropolitan areas. We used logistic regression analysis to estimate the association of Magnet recognition with organizational and market characteristics. Empirical results support hypotheses that adoption is positively associated with hospital complexity and specialization, as measured by teaching affiliation, and with hospital size, slack resources, and not-for-profit or public ownership (vs. for-profit). Adopters also are more likely to be located in markets that are experiencing population growth and are more likely to have competitor hospitals within the market that also have adopted Magnet status. A positive association of adoption with baccalaureate nursing school supply is contrary to the hypothesized relationship. Because of its rapid recent growth, consideration of Magnet program recognition should be on the strategic planning agenda of hospitals and hospital systems. Hospital administrators, particularly in smaller, for-profit hospitals, may expect more of their larger not-for-profit competitors, particularly teaching hospitals, to adopt Magnet recognition, increasing competition for baccalaureate-prepared registered nurses in the labor market.

Integration trumps selection in object recognition

Science.gov (United States)

Saarela, Toni P.; Landy, Michael S.

2015-01-01

Summary Finding and recognizing objects is a fundamental task of vision. Objects can be defined by several “cues” (color, luminance, texture etc.), and humans can integrate sensory cues to improve detection and recognition [1–3]. Cortical mechanisms fuse information from multiple cues [4], and shape-selective neural mechanisms can display cue-invariance by responding to a given shape independent of the visual cue defining it [5–8]. Selective attention, in contrast, improves recognition by isolating a subset of the visual information [9]. Humans can select single features (red or vertical) within a perceptual dimension (color or orientation), giving faster and more accurate responses to items having the attended feature [10,11]. Attention elevates neural responses and sharpens neural tuning to the attended feature, as shown by studies in psychophysics and modeling [11,12], imaging [13–16], and single-cell and neural population recordings [17,18]. Besides single features, attention can select whole objects [19–21]. Objects are among the suggested “units” of attention because attention to a single feature of an object causes the selection of all of its features [19–21]. Here, we pit integration against attentional selection in object recognition. We find, first, that humans can integrate information near-optimally from several perceptual dimensions (color, texture, luminance) to improve recognition. They cannot, however, isolate a single dimension even when the other dimensions provide task-irrelevant, potentially conflicting information. For object recognition, it appears that there is mandatory integration of information from multiple dimensions of visual experience. The advantage afforded by this integration, however, comes at the expense of attentional selection. PMID:25802154

Integration trumps selection in object recognition.

Science.gov (United States)

Saarela, Toni P; Landy, Michael S

2015-03-30

Finding and recognizing objects is a fundamental task of vision. Objects can be defined by several "cues" (color, luminance, texture, etc.), and humans can integrate sensory cues to improve detection and recognition [1-3]. Cortical mechanisms fuse information from multiple cues [4], and shape-selective neural mechanisms can display cue invariance by responding to a given shape independent of the visual cue defining it [5-8]. Selective attention, in contrast, improves recognition by isolating a subset of the visual information [9]. Humans can select single features (red or vertical) within a perceptual dimension (color or orientation), giving faster and more accurate responses to items having the attended feature [10, 11]. Attention elevates neural responses and sharpens neural tuning to the attended feature, as shown by studies in psychophysics and modeling [11, 12], imaging [13-16], and single-cell and neural population recordings [17, 18]. Besides single features, attention can select whole objects [19-21]. Objects are among the suggested "units" of attention because attention to a single feature of an object causes the selection of all of its features [19-21]. Here, we pit integration against attentional selection in object recognition. We find, first, that humans can integrate information near optimally from several perceptual dimensions (color, texture, luminance) to improve recognition. They cannot, however, isolate a single dimension even when the other dimensions provide task-irrelevant, potentially conflicting information. For object recognition, it appears that there is mandatory integration of information from multiple dimensions of visual experience. The advantage afforded by this integration, however, comes at the expense of attentional selection. Copyright © 2015 Elsevier Ltd. All rights reserved.

Quality based approach for adaptive face recognition

Science.gov (United States)

Abboud, Ali J.; Sellahewa, Harin; Jassim, Sabah A.

2009-05-01

Recent advances in biometric technology have pushed towards more robust and reliable systems. We aim to build systems that have low recognition errors and are less affected by variation in recording conditions. Recognition errors are often attributed to the usage of low quality biometric samples. Hence, there is a need to develop new intelligent techniques and strategies to automatically measure/quantify the quality of biometric image samples and if necessary restore image quality according to the need of the intended application. In this paper, we present no-reference image quality measures in the spatial domain that have impact on face recognition. The first is called symmetrical adaptive local quality index (SALQI) and the second is called middle halve (MH). Also, an adaptive strategy has been developed to select the best way to restore the image quality, called symmetrical adaptive histogram equalization (SAHE). The main benefits of using quality measures for adaptive strategy are: (1) avoidance of excessive unnecessary enhancement procedures that may cause undesired artifacts, and (2) reduced computational complexity which is essential for real time applications. We test the success of the proposed measures and adaptive approach for a wavelet-based face recognition system that uses the nearest neighborhood classifier. We shall demonstrate noticeable improvements in the performance of adaptive face recognition system over the corresponding non-adaptive scheme.

Facial Expression Recognition Through Machine Learning

Directory of Open Access Journals (Sweden)

Nazia Perveen

2015-08-01

Full Text Available Facial expressions communicate non-verbal cues which play an important role in interpersonal relations. Automatic recognition of facial expressions can be an important element of normal human-machine interfaces it might likewise be utilized as a part of behavioral science and in clinical practice. In spite of the fact that people perceive facial expressions for all intents and purposes immediately solid expression recognition by machine is still a challenge. From the point of view of automatic recognition a facial expression can be considered to comprise of disfigurements of the facial parts and their spatial relations or changes in the faces pigmentation. Research into automatic recognition of the facial expressions addresses the issues encompassing the representation and arrangement of static or dynamic qualities of these distortions or face pigmentation. We get results by utilizing the CVIPtools. We have taken train data set of six facial expressions of three persons and for train data set purpose we have total border mask sample 90 and 30 border mask sample for test data set purpose and we use RST- Invariant features and texture features for feature analysis and then classified them by using k- Nearest Neighbor classification algorithm. The maximum accuracy is 90.

Transfer learning for bimodal biometrics recognition

Science.gov (United States)

Dan, Zhiping; Sun, Shuifa; Chen, Yanfei; Gan, Haitao

2013-10-01

Biometrics recognition aims to identify and predict new personal identities based on their existing knowledge. As the use of multiple biometric traits of the individual may enables more information to be used for recognition, it has been proved that multi-biometrics can produce higher accuracy than single biometrics. However, a common problem with traditional machine learning is that the training and test data should be in the same feature space, and have the same underlying distribution. If the distributions and features are different between training and future data, the model performance often drops. In this paper, we propose a transfer learning method for face recognition on bimodal biometrics. The training and test samples of bimodal biometric images are composed of the visible light face images and the infrared face images. Our algorithm transfers the knowledge across feature spaces, relaxing the assumption of same feature space as well as same underlying distribution by automatically learning a mapping between two different but somewhat similar face images. According to the experiments in the face images, the results show that the accuracy of face recognition has been greatly improved by the proposed method compared with the other previous methods. It demonstrates the effectiveness and robustness of our method.

Hippocampal histone acetylation regulates object recognition and the estradiol-induced enhancement of object recognition.

Science.gov (United States)

Zhao, Zaorui; Fan, Lu; Fortress, Ashley M; Boulware, Marissa I; Frick, Karyn M

2012-02-15

Histone acetylation has recently been implicated in learning and memory processes, yet necessity of histone acetylation for such processes has not been demonstrated using pharmacological inhibitors of histone acetyltransferases (HATs). As such, the present study tested whether garcinol, a potent HAT inhibitor in vitro, could impair hippocampal memory consolidation and block the memory-enhancing effects of the modulatory hormone 17β-estradiol E2. We first showed that bilateral infusion of garcinol (0.1, 1, or 10 μg/side) into the dorsal hippocampus (DH) immediately after training impaired object recognition memory consolidation in ovariectomized female mice. A behaviorally effective dose of garcinol (10 μg/side) also significantly decreased DH HAT activity. We next examined whether DH infusion of a behaviorally subeffective dose of garcinol (1 ng/side) could block the effects of DH E2 infusion on object recognition and epigenetic processes. Immediately after training, ovariectomized female mice received bilateral DH infusions of vehicle, E2 (5 μg/side), garcinol (1 ng/side), or E2 plus garcinol. Forty-eight hours later, garcinol blocked the memory-enhancing effects of E2. Garcinol also reversed the E2-induced increase in DH histone H3 acetylation, HAT activity, and levels of the de novo methyltransferase DNMT3B, as well as the E2-induced decrease in levels of the memory repressor protein histone deacetylase 2. Collectively, these findings suggest that histone acetylation is critical for object recognition memory consolidation and the beneficial effects of E2 on object recognition. Importantly, this work demonstrates that the role of histone acetylation in memory processes can be studied using a HAT inhibitor.

Pose-Invariant Face Recognition via RGB-D Images.

Science.gov (United States)

Sang, Gaoli; Li, Jing; Zhao, Qijun

2016-01-01

Three-dimensional (3D) face models can intrinsically handle large pose face recognition problem. In this paper, we propose a novel pose-invariant face recognition method via RGB-D images. By employing depth, our method is able to handle self-occlusion and deformation, both of which are challenging problems in two-dimensional (2D) face recognition. Texture images in the gallery can be rendered to the same view as the probe via depth. Meanwhile, depth is also used for similarity measure via frontalization and symmetric filling. Finally, both texture and depth contribute to the final identity estimation. Experiments on Bosphorus, CurtinFaces, Eurecom, and Kiwi databases demonstrate that the additional depth information has improved the performance of face recognition with large pose variations and under even more challenging conditions.

Subspace methods for pattern recognition in intelligent environment

CERN Document Server

Jain, Lakhmi

2014-01-01

This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. With the fast development of internet and computer technologies, the amount of available data is rapidly increasing in our daily life. How to extract core information or useful features is an important issue. Subspace methods are widely used for dimension reduction and feature extraction in pattern recognition. They transform a high-dimensional data to a lower-dimensional space (subspace), where most information is retained. The book covers a broad spectrum of subspace methods including linear, nonlinear and multilinear subspace learning methods and applications. The applications include face alignment, face recognition, medical image analysis, remote sensing image classification, traffic sign recognition, image clustering, super resolution, edge detection, multi-view facial image synthesis.

The nature of visual self-recognition.

Science.gov (United States)

Suddendorf, Thomas; Butler, David L

2013-03-01

Visual self-recognition is often controversially cited as an indicator of self-awareness and assessed with the mirror-mark test. Great apes and humans, unlike small apes and monkeys, have repeatedly passed mirror tests, suggesting that the underlying brain processes are homologous and evolved 14-18 million years ago. However, neuroscientific, developmental, and clinical dissociations show that the medium used for self-recognition (mirror vs photograph vs video) significantly alters behavioral and brain responses, likely due to perceptual differences among the different media and prior experience. On the basis of this evidence and evolutionary considerations, we argue that the visual self-recognition skills evident in humans and great apes are a byproduct of a general capacity to collate representations, and need not index other aspects of self-awareness. Copyright © 2013 Elsevier Ltd. All rights reserved.

Classification and recognition of handwritten digits by using ...

Indian Academy of Sciences (India)

The problem of handwriting recognition has been studied for decades and ... tially completed work of character recognition using mathematical morphology. ... There are ten digits in English language and each digit is differentiated from the ...

Recognition without Awareness: Encoding and Retrieval Factors

Science.gov (United States)

Craik, Fergus I. M.; Rose, Nathan S.; Gopie, Nigel

2015-01-01

The article reports 4 experiments that explore the notion of recognition without awareness using words as the material. Previous work by Voss and associates has shown that complex visual patterns were correctly selected as targets in a 2-alternative forced-choice (2-AFC) recognition test although participants reported that they were guessing. The…

Object Recognition Memory and the Rodent Hippocampus

Science.gov (United States)

Broadbent, Nicola J.; Gaskin, Stephane; Squire, Larry R.; Clark, Robert E.

2010-01-01

In rodents, the novel object recognition task (NOR) has become a benchmark task for assessing recognition memory. Yet, despite its widespread use, a consensus has not developed about which brain structures are important for task performance. We assessed both the anterograde and retrograde effects of hippocampal lesions on performance in the NOR…

Gender-Based Prototype Formation in Face Recognition

Science.gov (United States)

Baudouin, Jean-Yves; Brochard, Renaud

2011-01-01

The role of gender categories in prototype formation during face recognition was investigated in 2 experiments. The participants were asked to learn individual faces and then to recognize them. During recognition, individual faces were mixed with faces, which were blended faces of same or different genders. The results of the 2 experiments showed…

Container-code recognition system based on computer vision and deep neural networks

Science.gov (United States)

Liu, Yi; Li, Tianjian; Jiang, Li; Liang, Xiaoyao

2018-04-01

Automatic container-code recognition system becomes a crucial requirement for ship transportation industry in recent years. In this paper, an automatic container-code recognition system based on computer vision and deep neural networks is proposed. The system consists of two modules, detection module and recognition module. The detection module applies both algorithms based on computer vision and neural networks, and generates a better detection result through combination to avoid the drawbacks of the two methods. The combined detection results are also collected for online training of the neural networks. The recognition module exploits both character segmentation and end-to-end recognition, and outputs the recognition result which passes the verification. When the recognition module generates false recognition, the result will be corrected and collected for online training of the end-to-end recognition sub-module. By combining several algorithms, the system is able to deal with more situations, and the online training mechanism can improve the performance of the neural networks at runtime. The proposed system is able to achieve 93% of overall recognition accuracy.

Object recognition in images by human vision and computer vision

NARCIS (Netherlands)

Chen, Q.; Dijkstra, J.; Vries, de B.

2010-01-01

Object recognition plays a major role in human behaviour research in the built environment. Computer based object recognition techniques using images as input are challenging, but not an adequate representation of human vision. This paper reports on the differences in object shape recognition

Automated smartphone audiometry: Validation of a word recognition test app.

Science.gov (United States)

Dewyer, Nicholas A; Jiradejvong, Patpong; Henderson Sabes, Jennifer; Limb, Charles J

2018-03-01

Develop and validate an automated smartphone word recognition test. Cross-sectional case-control diagnostic test comparison. An automated word recognition test was developed as an app for a smartphone with earphones. English-speaking adults with recent audiograms and various levels of hearing loss were recruited from an audiology clinic and were administered the smartphone word recognition test. Word recognition scores determined by the smartphone app and the gold standard speech audiometry test performed by an audiologist were compared. Test scores for 37 ears were analyzed. Word recognition scores determined by the smartphone app and audiologist testing were in agreement, with 86% of the data points within a clinically acceptable margin of error and a linear correlation value between test scores of 0.89. The WordRec automated smartphone app accurately determines word recognition scores. 3b. Laryngoscope, 128:707-712, 2018. © 2017 The American Laryngological, Rhinological and Otological Society, Inc.

Simultaneous tracking and activity recognition

DEFF Research Database (Denmark)

Manfredotti, Cristina Elena; Fleet, David J.; Hamilton, Howard J.

2011-01-01

be used to improve the prediction step of the tracking, while, at the same time, tracking information can be used for online activity recognition. Experimental results in two different settings show that our approach 1) decreases the error rate and improves the identity maintenance of the positional......Many tracking problems involve several distinct objects interacting with each other. We develop a framework that takes into account interactions between objects allowing the recognition of complex activities. In contrast to classic approaches that consider distinct phases of tracking and activity...... tracking and 2) identifies the correct activity with higher accuracy than standard approaches....

Human ear recognition by computer

CERN Document Server

Bhanu, Bir; Chen, Hui

2010-01-01

Biometrics deals with recognition of individuals based on their physiological or behavioral characteristics. The human ear is a new feature in biometrics that has several merits over the more common face, fingerprint and iris biometrics. Unlike the fingerprint and iris, it can be easily captured from a distance without a fully cooperative subject, although sometimes it may be hidden with hair, scarf and jewellery. Also, unlike a face, the ear is a relatively stable structure that does not change much with the age and facial expressions. ""Human Ear Recognition by Computer"" is the first book o

A Survey of Online Activity Recognition Using Mobile Phones

Directory of Open Access Journals (Sweden)

Muhammad Shoaib

2015-01-01

Full Text Available Physical activity recognition using embedded sensors has enabled many context-aware applications in different areas, such as healthcare. Initially, one or more dedicated wearable sensors were used for such applications. However, recently, many researchers started using mobile phones for this purpose, since these ubiquitous devices are equipped with various sensors, ranging from accelerometers to magnetic field sensors. In most of the current studies, sensor data collected for activity recognition are analyzed offline using machine learning tools. However, there is now a trend towards implementing activity recognition systems on these devices in an online manner, since modern mobile phones have become more powerful in terms of available resources, such as CPU, memory and battery. The research on offline activity recognition has been reviewed in several earlier studies in detail. However, work done on online activity recognition is still in its infancy and is yet to be reviewed. In this paper, we review the studies done so far that implement activity recognition systems on mobile phones and use only their on-board sensors. We discuss various aspects of these studies. Moreover, we discuss their limitations and present various recommendations for future research.

A Survey of Online Activity Recognition Using Mobile Phones

Science.gov (United States)

Shoaib, Muhammad; Bosch, Stephan; Incel, Ozlem Durmaz; Scholten, Hans; Havinga, Paul J.M.

2015-01-01

Physical activity recognition using embedded sensors has enabled many context-aware applications in different areas, such as healthcare. Initially, one or more dedicated wearable sensors were used for such applications. However, recently, many researchers started using mobile phones for this purpose, since these ubiquitous devices are equipped with various sensors, ranging from accelerometers to magnetic field sensors. In most of the current studies, sensor data collected for activity recognition are analyzed offline using machine learning tools. However, there is now a trend towards implementing activity recognition systems on these devices in an online manner, since modern mobile phones have become more powerful in terms of available resources, such as CPU, memory and battery. The research on offline activity recognition has been reviewed in several earlier studies in detail. However, work done on online activity recognition is still in its infancy and is yet to be reviewed. In this paper, we review the studies done so far that implement activity recognition systems on mobile phones and use only their on-board sensors. We discuss various aspects of these studies. Moreover, we discuss their limitations and present various recommendations for future research. PMID:25608213

Impaired processing of self-face recognition in anorexia nervosa.

Science.gov (United States)

Hirot, France; Lesage, Marine; Pedron, Lya; Meyer, Isabelle; Thomas, Pierre; Cottencin, Olivier; Guardia, Dewi

2016-03-01

Body image disturbances and massive weight loss are major clinical symptoms of anorexia nervosa (AN). The aim of the present study was to examine the influence of body changes and eating attitudes on self-face recognition ability in AN. Twenty-seven subjects suffering from AN and 27 control participants performed a self-face recognition task (SFRT). During the task, digital morphs between their own face and a gender-matched unfamiliar face were presented in a random sequence. Participants' self-face recognition failures, cognitive flexibility, body concern and eating habits were assessed with the Self-Face Recognition Questionnaire (SFRQ), Trail Making Test (TMT), Body Shape Questionnaire (BSQ) and Eating Disorder Inventory-2 (EDI-2), respectively. Subjects suffering from AN exhibited significantly greater difficulties than control participants in identifying their own face (p = 0.028). No significant difference was observed between the two groups for TMT (all p > 0.1, non-significant). Regarding predictors of self-face recognition skills, there was a negative correlation between SFRT and body mass index (p = 0.01) and a positive correlation between SFRQ and EDI-2 (p face recognition.

Bio-recognitive photonics of a DNA-guided organic semiconductor

Science.gov (United States)

Back, Seung Hyuk; Park, Jin Hyuk; Cui, Chunzhi; Ahn, Dong June

2016-01-01

Incorporation of duplex DNA with higher molecular weights has attracted attention for a new opportunity towards a better organic light-emitting diode (OLED) capability. However, biological recognition by OLED materials is yet to be addressed. In this study, specific oligomeric DNA-DNA recognition is successfully achieved by tri (8-hydroxyquinoline) aluminium (Alq3), an organic semiconductor. Alq3 rods crystallized with guidance from single-strand DNA molecules show, strikingly, a unique distribution of the DNA molecules with a shape of an `inverted' hourglass. The crystal's luminescent intensity is enhanced by 1.6-fold upon recognition of the perfect-matched target DNA sequence, but not in the case of a single-base mismatched one. The DNA-DNA recognition forming double-helix structure is identified to occur only in the rod's outer periphery. This study opens up new opportunities of Alq3, one of the most widely used OLED materials, enabling biological recognition.

Bio-recognitive photonics of a DNA-guided organic semiconductor.

Science.gov (United States)

Back, Seung Hyuk; Park, Jin Hyuk; Cui, Chunzhi; Ahn, Dong June

2016-01-04

Incorporation of duplex DNA with higher molecular weights has attracted attention for a new opportunity towards a better organic light-emitting diode (OLED) capability. However, biological recognition by OLED materials is yet to be addressed. In this study, specific oligomeric DNA-DNA recognition is successfully achieved by tri (8-hydroxyquinoline) aluminium (Alq3), an organic semiconductor. Alq3 rods crystallized with guidance from single-strand DNA molecules show, strikingly, a unique distribution of the DNA molecules with a shape of an 'inverted' hourglass. The crystal's luminescent intensity is enhanced by 1.6-fold upon recognition of the perfect-matched target DNA sequence, but not in the case of a single-base mismatched one. The DNA-DNA recognition forming double-helix structure is identified to occur only in the rod's outer periphery. This study opens up new opportunities of Alq3, one of the most widely used OLED materials, enabling biological recognition.

Improving Negative Emotion Recognition in Young Offenders Reduces Subsequent Crime.

Science.gov (United States)

Hubble, Kelly; Bowen, Katharine L; Moore, Simon C; van Goozen, Stephanie H M

2015-01-01

Children with antisocial behaviour show deficits in the perception of emotional expressions in others that may contribute to the development and persistence of antisocial and aggressive behaviour. Current treatments for antisocial youngsters are limited in effectiveness. It has been argued that more attention should be devoted to interventions that target neuropsychological correlates of antisocial behaviour. This study examined the effect of emotion recognition training on criminal behaviour. Emotion recognition and crime levels were studied in 50 juvenile offenders. Whilst all young offenders received their statutory interventions as the study was conducted, a subgroup of twenty-four offenders also took part in a facial affect training aimed at improving emotion recognition. Offenders in the training and control groups were matched for age, SES, IQ and lifetime crime level. All offenders were tested twice for emotion recognition performance, and recent crime data were collected after the testing had been completed. Before the training there were no differences between the groups in emotion recognition, with both groups displaying poor fear, sadness and anger recognition. After the training fear, sadness and anger recognition improved significantly in juvenile offenders in the training group. Although crime rates dropped in all offenders in the 6 months following emotion testing, only the group of offenders who had received the emotion training showed a significant reduction in the severity of the crimes they committed. The study indicates that emotion recognition can be relatively easily improved in youths who engage in serious antisocial and criminal behavior. The results suggest that improved emotion recognition has the potential to reduce the severity of reoffending.

Enhancing spoken connected-digit recognition accuracy by error ...

Indian Academy of Sciences (India)

R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22

nition systems have gained acceptable accuracy levels, the accuracy of recognition of current connected ... bar code and ISBN1 library code to name a few. ..... Kopec G, Bush M 1985 Network-based connected-digit recognition. IEEE Trans.

Sleep Enhances Explicit Recollection in Recognition Memory

Science.gov (United States)

Drosopoulos, Spyridon; Wagner, Ullrich; Born, Jan

2005-01-01

Recognition memory is considered to be supported by two different memory processes, i.e., the explicit recollection of information about a previous event and an implicit process of recognition based on a contextual sense of familiarity. Both types of memory supposedly rely on distinct memory systems. Sleep is known to enhance the consolidation of…

Advertisement and Children: is Brand Logo Recognition of Children Effected their Nutritional Habits and Food Preferences?

Directory of Open Access Journals (Sweden)

Burcu Tokuc

2009-12-01

Full Text Available AIM: This study was conducted for determining the eating habits and food knowledge and rate of recognizing brand logos currently featured in promotional campaigns of the food industry of the school children. And to explore the relationship between the ability of recognizing brand logos currently featured in promotional campaigns of the food industry and eating habits, food knowledge and preferences. METHOD: This cross-sectional study was conducted in 15 primary schools in the provincial center of Edirne. The questionnaire form was composed of three parts. In the first part; the socio-demographic characteristics and nutritional habits, in the second part; food knowledge and preferences of the students and in the third part; ability to recognize brand logos were questioned. RESULTS: 1018 students were participated in the study. The results of the study confirmed the findings of previous studies that the diet of children aged 9-11 was poor. The majority of the children reported eating unhealthy foods such cips, crisps and fast-food and drinking fizzy drinks at least once a day. Children knew healthy foods (med:8 but did not prefer healthy foods (med:5. 84.9 % of the students recognized more than the half of the brand logos. A correlation was found between higher brand logo recognition and poorer eating behaviors and food knowledge. CONCLUSION: Nonetheless this study did not presented strong evidence to suggest a casual link between food advertising and poor diet; it indicated that food promotion may have an influence on children. [TAF Prev Med Bull 2009; 8(6.000: 459-464

Emotion recognition pattern in adolescent boys with attention-deficit/hyperactivity disorder.

Science.gov (United States)

Aspan, Nikoletta; Bozsik, Csilla; Gadoros, Julia; Nagy, Peter; Inantsy-Pap, Judit; Vida, Peter; Halasz, Jozsef

2014-01-01

Social and emotional deficits were recently considered as inherent features of individuals with attention-deficit hyperactivity disorder (ADHD), but only sporadic literature data exist on emotion recognition in adolescents with ADHD. The aim of the present study was to establish emotion recognition profile in adolescent boys with ADHD in comparison with control adolescents. Forty-four adolescent boys (13-16 years) participated in the study after informed consent; 22 boys had a clinical diagnosis of ADHD, while data were also assessed from 22 adolescent control boys matched for age and Raven IQ. Parent- and self-reported behavioral characteristics were assessed by the means of the Strengths and Difficulties Questionnaire. The recognition of six basic emotions was evaluated by the "Facial Expressions of Emotion-Stimuli and Tests." Compared to controls, adolescents with ADHD were more sensitive in the recognition of disgust and, worse in the recognition of fear and showed a tendency for impaired recognition of sadness. Hyperactivity measures showed an inverse correlation with fear recognition. Our data suggest that adolescent boys with ADHD have alterations in the recognition of specific emotions.

Very deep recurrent convolutional neural network for object recognition

Science.gov (United States)

Brahimi, Sourour; Ben Aoun, Najib; Ben Amar, Chokri

2017-03-01

In recent years, Computer vision has become a very active field. This field includes methods for processing, analyzing, and understanding images. The most challenging problems in computer vision are image classification and object recognition. This paper presents a new approach for object recognition task. This approach exploits the success of the Very Deep Convolutional Neural Network for object recognition. In fact, it improves the convolutional layers by adding recurrent connections. This proposed approach was evaluated on two object recognition benchmarks: Pascal VOC 2007 and CIFAR-10. The experimental results prove the efficiency of our method in comparison with the state of the art methods.

Description and recognition of patterns in stochastic signals. [Electroencephalograms

Energy Technology Data Exchange (ETDEWEB)

Flik, T [Technische Univ. Berlin (F.R. Germany). Informatik-Forschungsgruppe Rechnerorganisation und Schaltwerke

1975-10-01

A method is shown for the description and recognition of patterns in stochastic signals such as electroencephalograms. For pattern extraction the signal is segmented at times of minimum amplitudes. The describing features consist of geometric values of the so defined patterns. The classification algorithm is based on the regression analysis, which is well known in the field of character recognition. For an economic classification a method is proposed which reduces the number of features. The quality of this pattern recognition method is demonstrated by the detection of spike wave complexes in electroencephalograms. The pattern description and recognition are provided for processing on a digital computer. (DE)

Haar-like Features for Robust Real-Time Face Recognition

DEFF Research Database (Denmark)

Nasrollahi, Kamal; Moeslund, Thomas B.

2013-01-01

Face recognition is still a very challenging task when the input face image is noisy, occluded by some obstacles, of very low-resolution, not facing the camera, and not properly illuminated. These problems make the feature extraction and consequently the face recognition system unstable....... The proposed system in this paper introduces the novel idea of using Haar-like features, which have commonly been used for object detection, along with a probabilistic classifier for face recognition. The proposed system is simple, real-time, effective and robust against most of the mentioned problems....... Experimental results on public databases show that the proposed system indeed outperforms the state-of-the-art face recognition systems....

High-performance speech recognition using consistency modeling

Science.gov (United States)

Digalakis, Vassilios; Murveit, Hy; Monaco, Peter; Neumeyer, Leo; Sankar, Ananth

1994-12-01

The goal of SRI's consistency modeling project is to improve the raw acoustic modeling component of SRI's DECIPHER speech recognition system and develop consistency modeling technology. Consistency modeling aims to reduce the number of improper independence assumptions used in traditional speech recognition algorithms so that the resulting speech recognition hypotheses are more self-consistent and, therefore, more accurate. At the initial stages of this effort, SRI focused on developing the appropriate base technologies for consistency modeling. We first developed the Progressive Search technology that allowed us to perform large-vocabulary continuous speech recognition (LVCSR) experiments. Since its conception and development at SRI, this technique has been adopted by most laboratories, including other ARPA contracting sites, doing research on LVSR. Another goal of the consistency modeling project is to attack difficult modeling problems, when there is a mismatch between the training and testing phases. Such mismatches may include outlier speakers, different microphones and additive noise. We were able to either develop new, or transfer and evaluate existing, technologies that adapted our baseline genonic HMM recognizer to such difficult conditions.

Forensic Face Recognition : From characteristic descriptors to strength of evidence

NARCIS (Netherlands)

Zeinstra, Christopher Gerard

2017-01-01

Forensic Face Recognition (FFR) is the use of biometric face recognition for several appli- cations in forensic science. Biometric face recognition uses the face modality as a means to discriminate between human beings; forensic science is the application of science and tech- nology to law

Facial emotion recognition in paranoid schizophrenia and autism spectrum disorder.

Science.gov (United States)

Sachse, Michael; Schlitt, Sabine; Hainz, Daniela; Ciaramidaro, Angela; Walter, Henrik; Poustka, Fritz; Bölte, Sven; Freitag, Christine M

2014-11-01

Schizophrenia (SZ) and autism spectrum disorder (ASD) share deficits in emotion processing. In order to identify convergent and divergent mechanisms, we investigated facial emotion recognition in SZ, high-functioning ASD (HFASD), and typically developed controls (TD). Different degrees of task difficulty and emotion complexity (face, eyes; basic emotions, complex emotions) were used. Two Benton tests were implemented in order to elicit potentially confounding visuo-perceptual functioning and facial processing. Nineteen participants with paranoid SZ, 22 with HFASD and 20 TD were included, aged between 14 and 33 years. Individuals with SZ were comparable to TD in all obtained emotion recognition measures, but showed reduced basic visuo-perceptual abilities. The HFASD group was impaired in the recognition of basic and complex emotions compared to both, SZ and TD. When facial identity recognition was adjusted for, group differences remained for the recognition of complex emotions only. Our results suggest that there is a SZ subgroup with predominantly paranoid symptoms that does not show problems in face processing and emotion recognition, but visuo-perceptual impairments. They also confirm the notion of a general facial and emotion recognition deficit in HFASD. No shared emotion recognition deficit was found for paranoid SZ and HFASD, emphasizing the differential cognitive underpinnings of both disorders. Copyright © 2014 Elsevier B.V. All rights reserved.

Representing Objects using Global 3D Relational Features for Recognition Tasks

DEFF Research Database (Denmark)

Mustafa, Wail

2015-01-01

representations. For representing objects, we derive global descriptors encoding shape using viewpoint-invariant features obtained from multiple sensors observing the scene. Objects are also described using color independently. This allows for combining color and shape when it is required for the task. For more...... robust color description, color calibration is performed. The framework was used in three recognition tasks: object instance recognition, object category recognition, and object spatial relationship recognition. For the object instance recognition task, we present a system that utilizes color and scale...

fMRI characterization of visual working memory recognition.

Science.gov (United States)

Rahm, Benjamin; Kaiser, Jochen; Unterrainer, Josef M; Simon, Juliane; Bledowski, Christoph

2014-04-15

Encoding and maintenance of information in visual working memory have been extensively studied, highlighting the crucial and capacity-limiting role of fronto-parietal regions. In contrast, the neural basis of recognition in visual working memory has remained largely unspecified. Cognitive models suggest that recognition relies on a matching process that compares sensory information with the mental representations held in memory. To characterize the neural basis of recognition we varied both the need for recognition and the degree of similarity between the probe item and the memory contents, while independently manipulating memory load to produce load-related fronto-parietal activations. fMRI revealed a fractionation of working memory functions across four distributed networks. First, fronto-parietal regions were activated independent of the need for recognition. Second, anterior parts of load-related parietal regions contributed to recognition but their activations were independent of the difficulty of matching in terms of sample-probe similarity. These results argue against a key role of the fronto-parietal attention network in recognition. Rather the third group of regions including bilateral temporo-parietal junction, posterior cingulate cortex and superior frontal sulcus reflected demands on matching both in terms of sample-probe-similarity and the number of items to be compared. Also, fourth, bilateral motor regions and right superior parietal cortex showed higher activation when matching provided clear evidence for a decision. Together, the segregation between the well-known fronto-parietal activations attributed to attentional operations in working memory from those regions involved in matching supports the theoretical view of separable attentional and mnemonic contributions to working memory. Yet, the close theoretical and empirical correspondence to perceptual decision making may call for an explicit consideration of decision making mechanisms in

Speech emotion recognition methods: A literature review

Science.gov (United States)

Basharirad, Babak; Moradhaseli, Mohammadreza

2017-10-01

Recently, attention of the emotional speech signals research has been boosted in human machine interfaces due to availability of high computation capability. There are many systems proposed in the literature to identify the emotional state through speech. Selection of suitable feature sets, design of a proper classifications methods and prepare an appropriate dataset are the main key issues of speech emotion recognition systems. This paper critically analyzed the current available approaches of speech emotion recognition methods based on the three evaluating parameters (feature set, classification of features, accurately usage). In addition, this paper also evaluates the performance and limitations of available methods. Furthermore, it highlights the current promising direction for improvement of speech emotion recognition systems.

Indonesian Sign Language Number Recognition using SIFT Algorithm

Science.gov (United States)

Mahfudi, Isa; Sarosa, Moechammad; Andrie Asmara, Rosa; Azrino Gustalika, M.

2018-04-01

Indonesian sign language (ISL) is generally used for deaf individuals and poor people communication in communicating. They use sign language as their primary language which consists of 2 types of action: sign and finger spelling. However, not all people understand their sign language so that this becomes a problem for them to communicate with normal people. this problem also becomes a factor they are isolated feel from the social life. It needs a solution that can help them to be able to interacting with normal people. Many research that offers a variety of methods in solving the problem of sign language recognition based on image processing. SIFT (Scale Invariant Feature Transform) algorithm is one of the methods that can be used to identify an object. SIFT is claimed very resistant to scaling, rotation, illumination and noise. Using SIFT algorithm for Indonesian sign language recognition number result rate recognition to 82% with the use of a total of 100 samples image dataset consisting 50 sample for training data and 50 sample images for testing data. Change threshold value get affect the result of the recognition. The best value threshold is 0.45 with rate recognition of 94%.

How similar are recognition memory and inductive reasoning?

Science.gov (United States)

Hayes, Brett K; Heit, Evan

2013-07-01

Conventionally, memory and reasoning are seen as different types of cognitive activities driven by different processes. In two experiments, we challenged this view by examining the relationship between recognition memory and inductive reasoning involving multiple forms of similarity. A common study set (members of a conjunctive category) was followed by a test set containing old and new category members, as well as items that matched the study set on only one dimension. The study and test sets were presented under recognition or induction instructions. In Experiments 1 and 2, the inductive property being generalized was varied in order to direct attention to different dimensions of similarity. When there was no time pressure on decisions, patterns of positive responding were strongly affected by property type, indicating that different types of similarity were driving recognition and induction. By comparison, speeded judgments showed weaker property effects and could be explained by generalization based on overall similarity. An exemplar model, GEN-EX (GENeralization from EXamples), could account for both the induction and recognition data. These findings show that induction and recognition share core component processes, even when the tasks involve flexible forms of similarity.

Speech recognition technology: an outlook for human-to-machine interaction.

Science.gov (United States)

Erdel, T; Crooks, S

2000-01-01

Speech recognition, as an enabling technology in healthcare-systems computing, is a topic that has been discussed for quite some time, but is just now coming to fruition. Traditionally, speech-recognition software has been constrained by hardware, but improved processors and increased memory capacities are starting to remove some of these limitations. With these barriers removed, companies that create software for the healthcare setting have the opportunity to write more successful applications. Among the criticisms of speech-recognition applications are the high rates of error and steep training curves. However, even in the face of such negative perceptions, there remains significant opportunities for speech recognition to allow healthcare providers and, more specifically, physicians, to work more efficiently and ultimately spend more time with their patients and less time completing necessary documentation. This article will identify opportunities for inclusion of speech-recognition technology in the healthcare setting and examine major categories of speech-recognition software--continuous speech recognition, command and control, and text-to-speech. We will discuss the advantages and disadvantages of each area, the limitations of the software today, and how future trends might affect them.

Youth Attitude Tracking Study 1997: Propensity and Advertising Report

National Research Council Canada - National Science Library

Wilson, Michael

1998-01-01

.... Findings cover results from questions on enlistment propensity, advertising awareness, recruiter contact, slogan recognition, media habits, and Internet usage among 16- to 24- year-old American youth...

Youth Attitude Tracking Study: 1998 Propensity and Advertising Report

National Research Council Canada - National Science Library

Wilson, Michael

2000-01-01

.... Findings cover results from questions on enlistment propensity, advertising awareness, recruiter contact, slogan recognition, media habits, and Internet usage among 16- to 24- year-old American youth...

Dissociation between recognition and recall in developmental amnesia

Science.gov (United States)

Adlam, Anna-Lynne R.; Malloy, Megan; Mishkin, Mortimer; Vargha-Khadem, Faraneh

2009-01-01

Developmental amnesia (DA) is a memory disorder due to hypoxia/ischaemia-induced damage to the hippocampus early in life. To test the hypothesis that this disorder is associated with a disproportionate impairment in recall vis-à-vis recognition, we examined a group of 10 patients with DA on the Doors and People test, which affords a quantitative comparison between measures of the two memory processes. The results supported the hypothesis in that the patients showed a sharp, though not complete, recall-recognition dissociation, exhibiting impairment on both measures relative to their matched controls, but with a far greater loss in recall than in recognition. Whether their relatively spared recognition ability is due to restriction of their medial temporal lobe damage to the hippocampus or whether it is due instead to their early age at injury is still uncertain. PMID:19524088

Printed Persian Subword Recognition Using Wavelet Packet Descriptors

Directory of Open Access Journals (Sweden)

Samira Nasrollahi

2013-01-01

Full Text Available In this paper, we present a new approach to offline OCR (optical character recognition for printed Persian subwords using wavelet packet transform. The proposed algorithm is used to extract font invariant and size invariant features from 87804 subwords of 4 fonts and 3 sizes. The feature vectors are compressed using PCA. The obtained feature vectors yield a pictorial dictionary for which an entry is the mean of each group that consists of the same subword with 4 fonts in 3 sizes. The sets of these features are congregated by combining them with the dot features for the recognition of printed Persian subwords. To evaluate the feature extraction results, this algorithm was tested on a set of 2000 subwords in printed Persian text documents. An encouraging recognition rate of 97.9% is got at subword level recognition.

Chemical Entity Recognition and Resolution to ChEBI

Science.gov (United States)

Grego, Tiago; Pesquita, Catia; Bastos, Hugo P.; Couto, Francisco M.

2012-01-01

Chemical entities are ubiquitous through the biomedical literature and the development of text-mining systems that can efficiently identify those entities are required. Due to the lack of available corpora and data resources, the community has focused its efforts in the development of gene and protein named entity recognition systems, but with the release of ChEBI and the availability of an annotated corpus, this task can be addressed. We developed a machine-learning-based method for chemical entity recognition and a lexical-similarity-based method for chemical entity resolution and compared them with Whatizit, a popular-dictionary-based method. Our methods outperformed the dictionary-based method in all tasks, yielding an improvement in F-measure of 20% for the entity recognition task, 2–5% for the entity-resolution task, and 15% for combined entity recognition and resolution tasks. PMID:25937941

Speed and automaticity of word recognition - inseparable twins?

DEFF Research Database (Denmark)

Poulsen, Mads; Asmussen, Vibeke; Elbro, Carsten

'Speed and automaticity' of word recognition is a standard collocation. However, it is not clear whether speed and automaticity (i.e., effortlessness) make independent contributions to reading comprehension. In theory, both speed and automaticity may save cognitive resources for comprehension...... processes. Hence, the aim of the present study was to assess the unique contributions of word recognition speed and automaticity to reading comprehension while controlling for decoding speed and accuracy. Method: 139 Grade 5 students completed tests of reading comprehension and computer-based tests of speed...... of decoding and word recognition together with a test of effortlessness (automaticity) of word recognition. Effortlessness was measured in a dual task in which participants were presented with a word enclosed in an unrelated figure. The task was to read the word and decide whether the figure was a triangle...

Pattern recognition methods in air pollution control

Energy Technology Data Exchange (ETDEWEB)

Tauber, S

1978-01-01

The use of pattern recognition methods for predicting air pollution developments is discussed. Computer analysis of historical pollution data allows comparison in graphical form. An example of crisis prediction for carbon monoxide concentrations, using the pattern recognition method of analysis, is presented. Results of the analysis agreed well with actual CO conditions. (6 graphs, 4 references, 1 table)

Using Maintenance Rehearsal to Explore Recognition Memory

Science.gov (United States)

Humphreys, Michael S.; Maguire, Angela M.; McFarlane, Kimberley A.; Burt, Jennifer S.; Bolland, Scott W.; Murray, Krista L.; Dunn, Ryan

2010-01-01

We examined associative and item recognition using the maintenance rehearsal paradigm. Our intent was to control for mnemonic strategies; to produce a low, graded level of learning; and to provide evidence of the role of attention in long-term memory. An advantage for low-frequency words emerged in both associative and item recognition at very low…

Face recognition in securing optical telecom network equipment

International Nuclear Information System (INIS)

Ali, N.M.

2015-01-01

In this paper, face recognition is used with a microcontroller based hardware module to secure the telecommunication equipments like ONU (optical network units) or any other telecommunication equipment. The face recognition classifier value optimization adaption is deployed and in this scheme by increasing or decreasing the number of images in the database will automatically generate and adopt the classifier value for recognition of known and unknown persons. On recognizing and unknown persons. On recognizing an unknown person, the hardware module will send an SMS to the concerned security personnel for security preventive measures. (author)

Gender Recognition from Unconstrained and Articulated Human Body

Directory of Open Access Journals (Sweden)

Qin Wu

2014-01-01

human body images acquired from an unconstrained environment in the real world. A systematic study of some critical issues in body-based gender recognition, such as which body parts are informative, how many body parts are needed to combine together, and what representations are good for articulated body-based gender recognition, is also presented. This paper also pursues data fusion schemes and efficient feature dimensionality reduction based on the partial least squares estimation. Extensive experiments are performed on two unconstrained databases which have not been explored before for gender recognition.

Automatic speech recognition for report generation in computed tomography

International Nuclear Information System (INIS)

Teichgraeber, U.K.M.; Ehrenstein, T.; Lemke, M.; Liebig, T.; Stobbe, H.; Hosten, N.; Keske, U.; Felix, R.

1999-01-01

Purpose: A study was performed to compare the performance of automatic speech recognition (ASR) with conventional transcription. Materials and Methods: 100 CT reports were generated by using ASR and 100 CT reports were dictated and written by medical transcriptionists. The time for dictation and correction of errors by the radiologist was assessed and the type of mistakes was analysed. The text recognition rate was calculated in both groups and the average time between completion of the imaging study by the technologist and generation of the written report was assessed. A commercially available speech recognition technology (ASKA Software, IBM Via Voice) running of a personal computer was used. Results: The time for the dictation using digital voice recognition was 9.4±2.3 min compared to 4.5±3.6 min with an ordinary Dictaphone. The text recognition rate was 97% with digital voice recognition and 99% with medical transcriptionists. The average time from imaging completion to written report finalisation was reduced from 47.3 hours with medical transcriptionists to 12.7 hours with ASR. The analysis of misspellings demonstrated (ASR vs. medical transcriptionists): 3 vs. 4 for syntax errors, 0 vs. 37 orthographic mistakes, 16 vs. 22 mistakes in substance and 47 vs. erroneously applied terms. Conclusions: The use of digital voice recognition as a replacement for medical transcription is recommendable when an immediate availability of written reports is necessary. (orig.) [de

Facial Expression Recognition

NARCIS (Netherlands)

Pantic, Maja; Li, S.; Jain, A.

2009-01-01

Facial expression recognition is a process performed by humans or computers, which consists of: 1. Locating faces in the scene (e.g., in an image; this step is also referred to as face detection), 2. Extracting facial features from the detected face region (e.g., detecting the shape of facial

Facial Expression Recognition Teaching to Preschoolers with Autism

DEFF Research Database (Denmark)

Christinaki, Eirini; Vidakis, Nikolaos; Triantafyllidis, Georgios

2013-01-01

The recognition of facial expressions is important for the perception of emotions. Understanding emotions is essential in human communication and social interaction. Children with autism have been reported to exhibit deficits in the recognition of affective expressions. Their difficulties...

Fingerprint Recognition Using Minutia Score Matching

OpenAIRE

J, Ravi.; Raja, K. B.; R, Venugopal. K.

2010-01-01

The popular Biometric used to authenticate a person is Fingerprint which is unique and permanent throughout a person’s life. A minutia matching is widely used for fingerprint recognition and can be classified as ridge ending and ridge bifurcation. In this paper we projected Fingerprint Recognition using Minutia Score Matching method (FRMSM). For Fingerprint thinning, the Block Filter is used, which scans the image at the boundary to preserves the quality of the image and extract the minutiae ...

Aggregating Local Descriptors for Epigraphs Recognition

OpenAIRE

Amato, Giuseppe; Falchi, Fabrizio; Rabitti, Fausto; Vadicamo, Lucia

2014-01-01

In this paper, we consider the task of recognizing epigraphs in images such as photos taken using mobile devices. Given a set of 17,155 photos related to 14,560 epigraphs, we used a k-NearestNeighbor approach in order to perform the recognition. The contribution of this work is in evaluating state-of-the-art visual object recognition techniques in this specific context. The experimental results conducted show that Vector of Locally Aggregated Descriptors obtained aggregating SIFT descriptors ...

Activity Recognition for Personal Time Management

Science.gov (United States)

Prekopcsák, Zoltán; Soha, Sugárka; Henk, Tamás; Gáspár-Papanek, Csaba

We describe an accelerometer based activity recognition system for mobile phones with a special focus on personal time management. We compare several data mining algorithms for the automatic recognition task in the case of single user and multiuser scenario, and improve accuracy with heuristics and advanced data mining methods. The results show that daily activities can be recognized with high accuracy and the integration with the RescueTime software can give good insights for personal time management.

The role of nitric oxide in the object recognition memory.

Science.gov (United States)

Pitsikas, Nikolaos

2015-05-15

The novel object recognition task (NORT) assesses recognition memory in animals. It is a non-rewarded paradigm that it is based on spontaneous exploratory behavior in rodents. This procedure is widely used for testing the effects of compounds on recognition memory. Recognition memory is a type of memory severely compromised in schizophrenic and Alzheimer's disease patients. Nitric oxide (NO) is sought to be an intra- and inter-cellular messenger in the central nervous system and its implication in learning and memory is well documented. Here I intended to critically review the role of NO-related compounds on different aspects of recognition memory. Current analysis shows that both NO donors and NO synthase (NOS) inhibitors are involved in object recognition memory and suggests that NO might be a promising target for cognition impairments. However, the potential neurotoxicity of NO would add a note of caution in this context. Copyright © 2014 Elsevier B.V. All rights reserved.

Online recognition of Chinese characters: the state-of-the-art.

Science.gov (United States)

Liu, Cheng-Lin; Jaeger, Stefan; Nakagawa, Masaki

2004-02-01

Online handwriting recognition is gaining renewed interest owing to the increase of pen computing applications and new pen input devices. The recognition of Chinese characters is different from western handwriting recognition and poses a special challenge. To provide an overview of the technical status and inspire future research, this paper reviews the advances in online Chinese character recognition (OLCCR), with emphasis on the research works from the 1990s. Compared to the research in the 1980s, the research efforts in the 1990s aimed to further relax the constraints of handwriting, namely, the adherence to standard stroke orders and stroke numbers and the restriction of recognition to isolated characters only. The target of recognition has shifted from regular script to fluent script in order to better meet the requirements of practical applications. The research works are reviewed in terms of pattern representation, character classification, learning/adaptation, and contextual processing. We compare important results and discuss possible directions of future research.

Enhancement of Iris Recognition System Based on Phase Only Correlation

Directory of Open Access Journals (Sweden)

Nuriza Pramita

2011-08-01

Full Text Available Iris recognition system is one of biometric based recognition/identification systems. Numerous techniques have been implemented to achieve a good recognition rate, including the ones based on Phase Only Correlation (POC. Significant and higher correlation peaks suggest that the system recognizes iris images of the same subject (person, while lower and unsignificant peaks correspond to recognition of those of difference subjects. Current POC methods have not investigated minimum iris point that can be used to achieve higher correlation peaks. This paper proposed a method that used only one-fourth of full normalized iris size to achieve higher (or at least the same recognition rate. Simulation on CASIA version 1.0 iris image database showed that averaged recognition rate of the proposed method achieved 67%, higher than that of using one-half (56% and full (53% iris point. Furthermore, all (100% POC peak values of the proposed method was higher than that of the method with full iris points.

A real time mobile-based face recognition with fisherface methods

Science.gov (United States)

Arisandi, D.; Syahputra, M. F.; Putri, I. L.; Purnamawati, S.; Rahmat, R. F.; Sari, P. P.

2018-03-01

Face Recognition is a field research in Computer Vision that study about learning face and determine the identity of the face from a picture sent to the system. By utilizing this face recognition technology, learning process about people’s identity between students in a university will become simpler. With this technology, student won’t need to browse student directory in university’s server site and look for the person with certain face trait. To obtain this goal, face recognition application use image processing methods consist of two phase, pre-processing phase and recognition phase. In pre-processing phase, system will process input image into the best image for recognition phase. Purpose of this pre-processing phase is to reduce noise and increase signal in image. Next, to recognize face phase, we use Fisherface Methods. This methods is chosen because of its advantage that would help system of its limited data. Therefore from experiment the accuracy of face recognition using fisherface is 90%.

Neuroticism and facial emotion recognition in healthy adults

NARCIS (Netherlands)

Andric, Sanja; Maric, Nadja P.; Knezevic, Goran; Mihaljevic, Marina; Mirjanic, Tijana; Velthorst, Eva; van Os, Jim

2016-01-01

The aim of the present study was to examine whether healthy individuals with higher levels of neuroticism, a robust independent predictor of psychopathology, exhibit altered facial emotion recognition performance. Facial emotion recognition accuracy was investigated in 104 healthy adults using the

Shopping Behavior Recognition using a Language Modeling Analogy

NARCIS (Netherlands)

Popa, M.C.; Rothkrantz, L.J.M.; Wiggers, P.; Shan, C.

2012-01-01

Automatic understanding and recognition of human shopping behavior has many potential applications, attracting an increasing interest inthe market- ing domain. The reliability and performance of the automatic recognition system is highly in uenced by the adopted theoretical model of behavior. In

EMPIRICAL STUDY OF CAR LICENSE PLATES RECOGNITION

Directory of Open Access Journals (Sweden)

Nasa Zata Dina

2015-01-01

Full Text Available The number of vehicles on the road has increased drastically in recent years. The license plate is an identity card for a vehicle. It can map to the owner and further information about vehicle. License plate information is useful to help traffic management systems. For example, traffic management systems can check for vehicles moving at speeds not permitted by law and can also be installed in parking areas to se-cure the entrance or exit way for vehicles. License plate recognition algorithms have been proposed by many researchers. License plate recognition requires license plate detection, segmentation, and charac-ters recognition. The algorithm detects the position of a license plate and extracts the characters. Various license plate recognition algorithms have been implemented, and each algorithm has its strengths and weaknesses. In this research, I implement three algorithms for detecting license plates, three algorithms for segmenting license plates, and two algorithms for recognizing license plate characters. I evaluate each of these algorithms on the same two datasets, one from Greece and one from Thailand. For detecting li-cense plates, the best result is obtained by a Haar cascade algorithm. After the best result of license plate detection is obtained, for the segmentation part a Laplacian based method has the highest accuracy. Last, the license plate recognition experiment shows that a neural network has better accuracy than other algo-rithm. I summarize and analyze the overall performance of each method for comparison.

Face Recognition Using Local Quantized Patterns and Gabor Filters

Science.gov (United States)

Khryashchev, V.; Priorov, A.; Stepanova, O.; Nikitin, A.

2015-05-01

The problem of face recognition in a natural or artificial environment has received a great deal of researchers' attention over the last few years. A lot of methods for accurate face recognition have been proposed. Nevertheless, these methods often fail to accurately recognize the person in difficult scenarios, e.g. low resolution, low contrast, pose variations, etc. We therefore propose an approach for accurate and robust face recognition by using local quantized patterns and Gabor filters. The estimation of the eye centers is used as a preprocessing stage. The evaluation of our algorithm on different samples from a standardized FERET database shows that our method is invariant to the general variations of lighting, expression, occlusion and aging. The proposed approach allows about 20% correct recognition accuracy increase compared with the known face recognition algorithms from the OpenCV library. The additional use of Gabor filters can significantly improve the robustness to changes in lighting conditions.

Demographic Recommendation by means of Group Profile Elicitation Using Speaker Age and Gender Recognition

DEFF Research Database (Denmark)

Shepstone, Sven Ewan; Tan, Zheng-Hua; Jensen, Søren Holdt

2013-01-01

, which itself is the input to a recommender system. The recommender system finds the content items whose demographics best match the group profile. We tested the effectiveness of the system for several typical home audience configurations. In a survey, users were given a configuration and asked to rate......In this paper we show a new method of using automatic age and gender recognition to recommend a sequence of multimedia items to a home TV audience comprising multiple viewers. Instead of relying on explicitly provided demographic data for each user, we define an audio-based demographic group...... a set of advertisements on how well each advertisement matched the configuration. Unbeknown to the subjects, half of the adverts were recommended using the derived audio demographics and the other half were randomly chosen. The recommended adverts received a significantly higher median rating of 7...

Activating the critical lure during study is unnecessary for false recognition.

Science.gov (United States)

Zeelenberg, René; Boot, Inge; Pecher, Diane

2005-06-01

Participants studied lists of nonwords (e.g., froost, floost, stoost, etc.) that were orthographic-phonologically similar to a nonpresented critical lure, which was also a nonword (e.g., ploost). Experiment 1 showed a high level of false recognition for the critical lure. Experiment 2 showed that the false recognition effect was also present for forewarned participants who were informed about the nature of the false recognition effect and told to avoid making false recognition judgments. The present results show that false recognition effects can be obtained even when the critical lure itself is not stored during study. This finding is problematic for accounts that attribute false memories to implicit associative responses or spreading activation but is easily explained by global familiarity models of recognition memory.

[Research progress of multi-model medical image fusion and recognition].

Science.gov (United States)

Zhou, Tao; Lu, Huiling; Chen, Zhiqiang; Ma, Jingxian

2013-10-01

Medical image fusion and recognition has a wide range of applications, such as focal location, cancer staging and treatment effect assessment. Multi-model medical image fusion and recognition are analyzed and summarized in this paper. Firstly, the question of multi-model medical image fusion and recognition is discussed, and its advantage and key steps are discussed. Secondly, three fusion strategies are reviewed from the point of algorithm, and four fusion recognition structures are discussed. Thirdly, difficulties, challenges and possible future research direction are discussed.

Comparing children's and adults' cognitive advertising competences in the Netherlands

NARCIS (Netherlands)

Rozendaal, E.; Buijzen, M.; Valkenburg, P.

2010-01-01

This study examines the age at which children reach adult levels of cognitive advertising competences. In a computer-assisted survey of 294 children (8-12 years) and 198 adults (18-30 years), we investigate at what age children reach adult levels of (1) advertising recognition, and (2) understanding

Secondary iris recognition method based on local energy-orientation feature

Science.gov (United States)

Huo, Guang; Liu, Yuanning; Zhu, Xiaodong; Dong, Hongxing

2015-01-01

This paper proposes a secondary iris recognition based on local features. The application of the energy-orientation feature (EOF) by two-dimensional Gabor filter to the extraction of the iris goes before the first recognition by the threshold of similarity, which sets the whole iris database into two categories-a correctly recognized class and a class to be recognized. Therefore, the former are accepted and the latter are transformed by histogram to achieve an energy-orientation histogram feature (EOHF), which is followed by a second recognition with the chi-square distance. The experiment has proved that the proposed method, because of its higher correct recognition rate, could be designated as the most efficient and effective among its companion studies in iris recognition algorithms.

Improving Negative Emotion Recognition in Young Offenders Reduces Subsequent Crime.

Directory of Open Access Journals (Sweden)

Kelly Hubble

Full Text Available Children with antisocial behaviour show deficits in the perception of emotional expressions in others that may contribute to the development and persistence of antisocial and aggressive behaviour. Current treatments for antisocial youngsters are limited in effectiveness. It has been argued that more attention should be devoted to interventions that target neuropsychological correlates of antisocial behaviour. This study examined the effect of emotion recognition training on criminal behaviour.Emotion recognition and crime levels were studied in 50 juvenile offenders. Whilst all young offenders received their statutory interventions as the study was conducted, a subgroup of twenty-four offenders also took part in a facial affect training aimed at improving emotion recognition. Offenders in the training and control groups were matched for age, SES, IQ and lifetime crime level. All offenders were tested twice for emotion recognition performance, and recent crime data were collected after the testing had been completed.Before the training there were no differences between the groups in emotion recognition, with both groups displaying poor fear, sadness and anger recognition. After the training fear, sadness and anger recognition improved significantly in juvenile offenders in the training group. Although crime rates dropped in all offenders in the 6 months following emotion testing, only the group of offenders who had received the emotion training showed a significant reduction in the severity of the crimes they committed.The study indicates that emotion recognition can be relatively easily improved in youths who engage in serious antisocial and criminal behavior. The results suggest that improved emotion recognition has the potential to reduce the severity of reoffending.

Face recognition increases during saccade preparation.

Science.gov (United States)

Lin, Hai; Rizak, Joshua D; Ma, Yuan-ye; Yang, Shang-chuan; Chen, Lin; Hu, Xin-tian

2014-01-01

Face perception is integral to human perception system as it underlies social interactions. Saccadic eye movements are frequently made to bring interesting visual information, such as faces, onto the fovea for detailed processing. Just before eye movement onset, the processing of some basic features, such as the orientation, of an object improves at the saccade landing point. Interestingly, there is also evidence that indicates faces are processed in early visual processing stages similar to basic features. However, it is not known whether this early enhancement of processing includes face recognition. In this study, three experiments were performed to map the timing of face presentation to the beginning of the eye movement in order to evaluate pre-saccadic face recognition. Faces were found to be similarly processed as simple objects immediately prior to saccadic movements. Starting ∼ 120 ms before a saccade to a target face, independent of whether or not the face was surrounded by other faces, the face recognition gradually improved and the critical spacing of the crowding decreased as saccade onset was approaching. These results suggest that an upcoming saccade prepares the visual system for new information about faces at the saccade landing site and may reduce the background in a crowd to target the intended face. This indicates an important role of pre-saccadic eye movement signals in human face recognition.

Pedestrian recognition using automotive radar sensors

Science.gov (United States)

Bartsch, A.; Fitzek, F.; Rasshofer, R. H.

2012-09-01

The application of modern series production automotive radar sensors to pedestrian recognition is an important topic in research on future driver assistance systems. The aim of this paper is to understand the potential and limits of such sensors in pedestrian recognition. This knowledge could be used to develop next generation radar sensors with improved pedestrian recognition capabilities. A new raw radar data signal processing algorithm is proposed that allows deep insights into the object classification process. The impact of raw radar data properties can be directly observed in every layer of the classification system by avoiding machine learning and tracking. This gives information on the limiting factors of raw radar data in terms of classification decision making. To accomplish the very challenging distinction between pedestrians and static objects, five significant and stable object features from the spatial distribution and Doppler information are found. Experimental results with data from a 77 GHz automotive radar sensor show that over 95% of pedestrians can be classified correctly under optimal conditions, which is compareable to modern machine learning systems. The impact of the pedestrian's direction of movement, occlusion, antenna beam elevation angle, linear vehicle movement, and other factors are investigated and discussed. The results show that under real life conditions, radar only based pedestrian recognition is limited due to insufficient Doppler frequency and spatial resolution as well as antenna side lobe effects.

Optical Character Recognition.

Science.gov (United States)

Converso, L.; Hocek, S.

1990-01-01

This paper describes computer-based optical character recognition (OCR) systems, focusing on their components (the computer, the scanner, the OCR, and the output device); how the systems work; and features to consider in selecting a system. A list of 26 questions to ask to evaluate systems for potential purchase is included. (JDD)

Real-Time Multiview Recognition of Human Gestures by Distributed Image Processing

Directory of Open Access Journals (Sweden)

Sato Kosuke

2010-01-01

Full Text Available Since a gesture involves a dynamic and complex motion, multiview observation and recognition are desirable. For the better representation of gestures, one needs to know, in the first place, from which views a gesture should be observed. Furthermore, it becomes increasingly important how the recognition results are integrated when larger numbers of camera views are considered. To investigate these problems, we propose a framework under which multiview recognition is carried out, and an integration scheme by which the recognition results are integrated online and in realtime. For performance evaluation, we use the ViHASi (Virtual Human Action Silhouette public image database as a benchmark and our Japanese sign language (JSL image database that contains 18 kinds of hand signs. By examining the recognition rates of each gesture for each view, we found gestures that exhibit view dependency and the gestures that do not. Also, we found that the view dependency itself could vary depending on the target gesture sets. By integrating the recognition results of different views, our swarm-based integration provides more robust and better recognition performance than individual fixed-view recognition agents.

WORD LEVEL DISCRIMINATIVE TRAINING FOR HANDWRITTEN WORD RECOGNITION

NARCIS (Netherlands)

Chen, W.; Gader, P.

2004-01-01

Word level training refers to the process of learning the parameters of a word recognition system based on word level criteria functions. Previously, researchers trained lexicon­driven handwritten word recognition systems at the character level individually. These systems generally use statistical

Postsurgical Disfigurement Influences Disgust Recognition: A Case-Control Study.

Science.gov (United States)

Lisan, Quentin; George, Nathalie; Hans, Stephane; Laccourreye, Ollivier; Lemogne, Cédric

Little is known about how emotion recognition may be modified in individuals prone to elicit disgust. We sought to determine if subjects with total laryngectomy would present a modified recognition of facial expressions of disgust. A total of 29 patients presenting with a history of advanced-stage laryngeal cancer were recruited, 17 being surgically treated (total laryngectomy) and 12 treated with chemoradiation therapy only. Based on a validated set of images of facial expressions of fear, disgust, surprise, happiness, sadness and anger displayed by 6 actors, we presented participants with expressions of each emotion at 5 levels of increasing intensity and measured their ability to recognize these emotions. Participants with (vs without) laryngectomy showed a higher threshold for the recognition of disgust (3.2. vs 2.7 images needed before emotion recognition, p = 0.03) and a lower success rate of correct recognition (75.5% vs 88.9%, p = 0.03). Subjects presenting with an aesthetic impairment of the head and neck showed poorer performance in disgust recognition when compared with those without disfigurement. These findings might relate either to some perceptual adaptation, habituation phenomenon, or to some higher-level processes related to emotion regulation strategies. Copyright © 2018 Academy of Consultation-Liaison Psychiatry. Published by Elsevier Inc. All rights reserved.

Technical Reviews on Pattern Recognition in Process Analytical Technology

International Nuclear Information System (INIS)

Kim, Jong Yun; Choi, Yong Suk; Ji, Sun Kyung; Park, Yong Joon; Song, Kyu Seok; Jung, Sung Hee

2008-12-01

Pattern recognition is one of the first and the most widely adopted chemometric tools among many active research area in chemometrics such as design of experiment(DoE), pattern recognition, multivariate calibration, signal processing. Pattern recognition has been used to identify the origin of a wine and the time of year that the vine was grown by using chromatography, cause of fire by using GC/MS chromatography, detection of explosives and land mines, cargo and luggage inspection in seaports and airports by using a prompt gamma-ray activation analysis, and source apportionment of environmental pollutant by using a stable isotope ratio mass spectrometry. Recently, pattern recognition has been taken into account as a major chemometric tool in the so-called 'process analytical technology (PAT)', which is a newly-developed concept in the area of process analytics proposed by US Food and Drug Administration (US FDA). For instance, identification of raw material by using a pattern recognition analysis plays an important role for the effective quality control of the production process. Recently, pattern recognition technique has been used to identify the spatial distribution and uniformity of the active ingredients present in the product such as tablet by transforming the chemical data into the visual information

Medial prefrontal cortex role in recognition memory in rodents.

Science.gov (United States)

Morici, Juan Facundo; Bekinschtein, Pedro; Weisstaub, Noelia V

2015-10-01

The study of the neurobiology of recognition memory, defined by the integration of the different components of experiences that support recollection of past experiences have been a challenge for memory researches for many years. In the last twenty years, with the development of the spontaneous novel object recognition task and all its variants this has started to change. The features of recognition memory include a particular object or person ("what"), the context in which the experience took place, which can be the arena itself or the location within a particular arena ("where") and the particular time at which the event occurred ("when"). This definition instead of the historical anthropocentric one allows the study of this type of episodic memory in animal models. Some forms of recognition memory that require integration of different features recruit the medial prefrontal cortex. Focusing on findings from spontaneous recognition memory tasks performed by rodents, this review concentrates on the description of previous works that have examined the role that the medial prefrontal cortex has on the different steps of recognition memory. We conclude that this structure, independently of the task used, is required at different memory stages when the task cannot be solved by a single item strategy. Copyright © 2015 Elsevier B.V. All rights reserved.

A Survey on Banknote Recognition Methods by Various Sensors

Science.gov (United States)

Lee, Ji Woo; Hong, Hyung Gil; Kim, Ki Wan; Park, Kang Ryoung

2017-01-01

Despite a decrease in the use of currency due to the recent growth in the use of electronic financial transactions, real money transactions remain very important in the global market. While performing transactions with real money, touching and counting notes by hand, is still a common practice in daily life, various types of automated machines, such as ATMs and banknote counters, are essential for large-scale and safe transactions. This paper presents studies that have been conducted in four major areas of research (banknote recognition, counterfeit banknote detection, serial number recognition, and fitness classification) in the accurate banknote recognition field by various sensors in such automated machines, and describes the advantages and drawbacks of the methods presented in those studies. While to a limited extent some surveys have been presented in previous studies in the areas of banknote recognition or counterfeit banknote recognition, this paper is the first of its kind to review all four areas. Techniques used in each of the four areas recognize banknote information (denomination, serial number, authenticity, and physical condition) based on image or sensor data, and are actually applied to banknote processing machines across the world. This study also describes the technological challenges faced by such banknote recognition techniques and presents future directions of research to overcome them. PMID:28208733

The Medial Dorsal Thalamic Nucleus and the Medial Prefrontal Cortex of the Rat Function Together to Support Associative Recognition and Recency but Not Item Recognition

Science.gov (United States)

Cross, Laura; Brown, Malcolm W.; Aggleton, John P.; Warburton, E. Clea

2013-01-01

In humans recognition memory deficits, a typical feature of diencephalic amnesia, have been tentatively linked to mediodorsal thalamic nucleus (MD) damage. Animal studies have occasionally investigated the role of the MD in single-item recognition, but have not systematically analyzed its involvement in other recognition memory processes. In…

Handwritten recognition of Tamil vowels using deep learning

Science.gov (United States)

Ram Prashanth, N.; Siddarth, B.; Ganesh, Anirudh; Naveen Kumar, Vaegae

2017-11-01

We come across a large volume of handwritten texts in our daily lives and handwritten character recognition has long been an important area of research in pattern recognition. The complexity of the task varies among different languages and it so happens largely due to the similarity between characters, distinct shapes and number of characters which are all language-specific properties. There have been numerous works on character recognition of English alphabets and with laudable success, but regional languages have not been dealt with very frequently and with similar accuracies. In this paper, we explored the performance of Deep Belief Networks in the classification of Handwritten Tamil vowels, and conclusively compared the results obtained. The proposed method has shown satisfactory recognition accuracy in light of difficulties faced with regional languages such as similarity between characters and minute nuances that differentiate them. We can further extend this to all the Tamil characters.

Facial Emotion Recognition Using Context Based Multimodal Approach

Directory of Open Access Journals (Sweden)

Priya Metri

2011-12-01

Full Text Available Emotions play a crucial role in person to person interaction. In recent years, there has been a growing interest in improving all aspects of interaction between humans and computers. The ability to understand human emotions is desirable for the computer in several applications especially by observing facial expressions. This paper explores a ways of human-computer interaction that enable the computer to be more aware of the user’s emotional expressions we present a approach for the emotion recognition from a facial expression, hand and body posture. Our model uses multimodal emotion recognition system in which we use two different models for facial expression recognition and for hand and body posture recognition and then combining the result of both classifiers using a third classifier which give the resulting emotion . Multimodal system gives more accurate result than a signal or bimodal system

Action Recognition by Joint Spatial-Temporal Motion Feature

Directory of Open Access Journals (Sweden)

Weihua Zhang

2013-01-01

Full Text Available This paper introduces a method for human action recognition based on optical flow motion features extraction. Automatic spatial and temporal alignments are combined together in order to encourage the temporal consistence on each action by an enhanced dynamic time warping (DTW algorithm. At the same time, a fast method based on coarse-to-fine DTW constraint to improve computational performance without reducing accuracy is induced. The main contributions of this study include (1 a joint spatial-temporal multiresolution optical flow computation method which can keep encoding more informative motion information than recent proposed methods, (2 an enhanced DTW method to improve temporal consistence of motion in action recognition, and (3 coarse-to-fine DTW constraint on motion features pyramids to speed up recognition performance. Using this method, high recognition accuracy is achieved on different action databases like Weizmann database and KTH database.

Forensic Speaker Recognition Law Enforcement and Counter-Terrorism

CERN Document Server

Patil, Hemant

2012-01-01

Forensic Speaker Recognition: Law Enforcement and Counter-Terrorism is an anthology of the research findings of 35 speaker recognition experts from around the world. The volume provides a multidimensional view of the complex science involved in determining whether a suspect’s voice truly matches forensic speech samples, collected by law enforcement and counter-terrorism agencies, that are associated with the commission of a terrorist act or other crimes. While addressing such topics as the challenges of forensic case work, handling speech signal degradation, analyzing features of speaker recognition to optimize voice verification system performance, and designing voice applications that meet the practical needs of law enforcement and counter-terrorism agencies, this material all sounds a common theme: how the rigors of forensic utility are demanding new levels of excellence in all aspects of speaker recognition. The contributors are among the most eminent scientists in speech engineering and signal process...

The Pandora software development kit for pattern recognition

Energy Technology Data Exchange (ETDEWEB)

Marshall, J.S.; Thomson, M.A. [University of Cambridge, Cavendish Laboratory, Cambridge (United Kingdom)

2015-09-15

The development of automated solutions to pattern recognition problems is important in many areas of scientific research and human endeavour. This paper describes the implementation of the Pandora software development kit, which aids the process of designing, implementing and running pattern recognition algorithms. The Pandora Application Programming Interfaces ensure simple specification of the building-blocks defining a pattern recognition problem. The logic required to solve the problem is implemented in algorithms. The algorithms request operations to create or modify data structures and the operations are performed by the Pandora framework. This design promotes an approach using many decoupled algorithms, each addressing specific topologies. Details of algorithms addressing two pattern recognition problems in High Energy Physics are presented: reconstruction of events at a high-energy e{sup +}e{sup -} linear collider and reconstruction of cosmic ray or neutrino events in a liquid argon time projection chamber. (orig.)

Iris analysis for biometric recognition systems

CERN Document Server

Bodade, Rajesh M

2014-01-01

The book presents three most significant areas in Biometrics and Pattern Recognition. A step-by-step approach for design and implementation of Dual Tree Complex Wavelet Transform (DTCWT) plus Rotated Complex Wavelet Filters (RCWF) is discussed in detail. In addition to the above, the book provides detailed analysis of iris images and two methods of iris segmentation. It also discusses simplified study of some subspace-based methods and distance measures for iris recognition backed by empirical studies and statistical success verifications.

Human action recognition with depth cameras

CERN Document Server

Wang, Jiang; Wu, Ying

2014-01-01

Action recognition technology has many real-world applications in human-computer interaction, surveillance, video retrieval, retirement home monitoring, and robotics. The commoditization of depth sensors has also opened up further applications that were not feasible before. This text focuses on feature representation and machine learning algorithms for action recognition from depth sensors. After presenting a comprehensive overview of the state of the art, the authors then provide in-depth descriptions of their recently developed feature representations and machine learning techniques, includi

Optical Pattern Recognition for Missile Guidance.

Science.gov (United States)

1982-11-15

directed to novel pattern recognition algo- rithms (that allow pattern recognition and object classification in the face of various geometrical and...I wats EF5 = 50) p.j/t’ni 2 (for btith image pat tern recognitio itas a preproicessing oiperatiton. Ini devices). TIhe rt’ad light intensity (0.33t mW...electrodes on its large faces . This Priz light modulator and the motivation for its devel- SLM is known as the Prom (Pockels real-time optical opment. In Sec

Real-Time Hand Posture Recognition Using a Range Camera

Science.gov (United States)

Lahamy, Herve

The basic goal of human computer interaction is to improve the interaction between users and computers by making computers more usable and receptive to the user's needs. Within this context, the use of hand postures in replacement of traditional devices such as keyboards, mice and joysticks is being explored by many researchers. The goal is to interpret human postures via mathematical algorithms. Hand posture recognition has gained popularity in recent years, and could become the future tool for humans to interact with computers or virtual environments. An exhaustive description of the frequently used methods available in literature for hand posture recognition is provided. It focuses on the different types of sensors and data used, the segmentation and tracking methods, the features used to represent the hand postures as well as the classifiers considered in the recognition process. Those methods are usually presented as highly robust with a recognition rate close to 100%. However, a couple of critical points necessary for a successful real-time hand posture recognition system require major improvement. Those points include the features used to represent the hand segment, the number of postures simultaneously recognizable, the invariance of the features with respect to rotation, translation and scale and also the behavior of the classifiers against non-perfect hand segments for example segments including part of the arm or missing part of the palm. A 3D time-of-flight camera named SR4000 has been chosen to develop a new methodology because of its capability to provide in real-time and at high frame rate 3D information on the scene imaged. This sensor has been described and evaluated for its capability for capturing in real-time a moving hand. A new recognition method that uses the 3D information provided by the range camera to recognize hand postures has been proposed. The different steps of this methodology including the segmentation, the tracking, the hand

The effect of word concreteness on recognition memory.

Science.gov (United States)

Fliessbach, K; Weis, S; Klaver, P; Elger, C E; Weber, B

2006-09-01

Concrete words that are readily imagined are better remembered than abstract words. Theoretical explanations for this effect either claim a dual coding of concrete words in the form of both a verbal and a sensory code (dual-coding theory), or a more accessible semantic network for concrete words than for abstract words (context-availability theory). However, the neural mechanisms of improved memory for concrete versus abstract words are poorly understood. Here, we investigated the processing of concrete and abstract words during encoding and retrieval in a recognition memory task using event-related functional magnetic resonance imaging (fMRI). As predicted, memory performance was significantly better for concrete words than for abstract words. Abstract words elicited stronger activations of the left inferior frontal cortex both during encoding and recognition than did concrete words. Stronger activation of this area was also associated with successful encoding for both abstract and concrete words. Concrete words elicited stronger activations bilaterally in the posterior inferior parietal lobe during recognition. The left parietal activation was associated with correct identification of old stimuli. The anterior precuneus, left cerebellar hemisphere and the posterior and anterior cingulate cortex showed activations both for successful recognition of concrete words and for online processing of concrete words during encoding. Additionally, we observed a correlation across subjects between brain activity in the left anterior fusiform gyrus and hippocampus during recognition of learned words and the strength of the concreteness effect. These findings support the idea of specific brain processes for concrete words, which are reactivated during successful recognition.

Contribution to automatic handwritten characters recognition. Application to optical moving characters recognition

International Nuclear Information System (INIS)

Gokana, Denis

1986-01-01

This paper describes a research work on computer aided vision relating to the design of a vision system which can recognize isolated handwritten characters written on a mobile support. We use a technique which consists in analyzing information contained in the contours of the polygon circumscribed to the character's shape. These contours are segmented and labelled to give a new set of features constituted by: - right and left 'profiles', - topological and algebraic unvarying properties. A new method of character's recognition induced from this representation based on a multilevel hierarchical technique is then described. In the primary level, we use a fuzzy classification with dynamic programming technique using 'profiles'. The other levels adjust the recognition by using topological and algebraic unvarying properties. Several results are presented and an accuracy of 99 pc was reached for handwritten numeral characters, thereby attesting the robustness of our algorithm. (author) [fr

Robust and Effective Component-based Banknote Recognition for the Blind.

Science.gov (United States)

Hasanuzzaman, Faiz M; Yang, Xiaodong; Tian, Yingli

2012-11-01

We develop a novel camera-based computer vision technology to automatically recognize banknotes for assisting visually impaired people. Our banknote recognition system is robust and effective with the following features: 1) high accuracy: high true recognition rate and low false recognition rate, 2) robustness: handles a variety of currency designs and bills in various conditions, 3) high efficiency: recognizes banknotes quickly, and 4) ease of use: helps blind users to aim the target for image capture. To make the system robust to a variety of conditions including occlusion, rotation, scaling, cluttered background, illumination change, viewpoint variation, and worn or wrinkled bills, we propose a component-based framework by using Speeded Up Robust Features (SURF). Furthermore, we employ the spatial relationship of matched SURF features to detect if there is a bill in the camera view. This process largely alleviates false recognition and can guide the user to correctly aim at the bill to be recognized. The robustness and generalizability of the proposed system is evaluated on a dataset including both positive images (with U.S. banknotes) and negative images (no U.S. banknotes) collected under a variety of conditions. The proposed algorithm, achieves 100% true recognition rate and 0% false recognition rate. Our banknote recognition system is also tested by blind users.

Face Age and Eye Gaze Influence Older Adults' Emotion Recognition.

Science.gov (United States)

Campbell, Anna; Murray, Janice E; Atkinson, Lianne; Ruffman, Ted

2017-07-01

Eye gaze has been shown to influence emotion recognition. In addition, older adults (over 65 years) are not as influenced by gaze direction cues as young adults (18-30 years). Nevertheless, these differences might stem from the use of young to middle-aged faces in emotion recognition research because older adults have an attention bias toward old-age faces. Therefore, using older face stimuli might allow older adults to process gaze direction cues to influence emotion recognition. To investigate this idea, young and older adults completed an emotion recognition task with young and older face stimuli displaying direct and averted gaze, assessing labeling accuracy for angry, disgusted, fearful, happy, and sad faces. Direct gaze rather than averted gaze improved young adults' recognition of emotions in young and older faces, but for older adults this was true only for older faces. The current study highlights the impact of stimulus face age and gaze direction on emotion recognition in young and older adults. The use of young face stimuli with direct gaze in most research might contribute to age-related emotion recognition differences. © The Author 2015. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.