Showing papers on "Facial recognition system published in 1986"

Understanding face recognition

Abstract: The aim of this paper is to develop a theoretical model and a set of terms for understanding and discussing how we recognize familiar faces, and the relationship between recognition and other aspects of face processing. It is suggested that there are seven distinct types of information that we derive from seen faces; these are labelled pictorial, structural, visually derived semantic, identity-specific semantic, name, expression and facial speech codes. A functional model is proposed in which structural encoding processes provide descriptions suitable for the analysis of facial speech, for analysis of expression and for face recognition units. Recognition of familiar faces involves a match between the products of structural encoding and previously stored structural codes describing the appearance of familiar faces, held in face recognition units. Identity-specific semantic codes are then accessed from person identity nodes, and subsequently name codes are retrieved. It is also proposed that the cognitive system plays an active role in deciding whether or not the initial match is sufficiently close to indicate true recognition or merely a ‘resemblance’; several factors are seen as influencing such decisions. This functional model is used to draw together data from diverse sources including laboratory experiments, studies of everyday errors, and studies of patients with different types of cerebral injury. It is also used to clarify similarities and differences between processes responsible for object, word and face recognition.

Aspects of face processing

Abstract: 1. Introduction.- to aspects of face processing: Ten questions in need of answers.- 2. Perceptual Processes.- Microgenesis of face perception..- Recognition memory transfer between spatial- frequency analyzed faces..- Reaction time measures of feature saliency in a perceptual integration task..- Perception of upside-down faces: An analysis from the viewpoint of cue-saliency..- 3. Memory Processes.- On the memorability of the human face..- Face recognition is not unique. Evidence from individual differences..- Lateral reversal and facial recognition memory: Are right-lookers special?.- Context effects in recognition memory of faces: Some theoretical problems..- 4. Cognitive Processes.- Recognising familiar faces..- Face recognition: More than a feeling of familiarity?.- Getting semantic information from familiar faces..- What happens when a face rings a bell?: The automatic processing of famous faces..- 5. Socio-Cognitive Factors.- Levels of representation and memory for faces..- Formation of facial prototypes..- Stereotyping and face memory..- The influence of race on face recognition..- Faces, prototypes, and additive tree representations..- 6. Cortical Specialisation.- Functional organization of visual neurones processing face identity..- Hemispheric asymmetry in face processing in infancy..- Models of laterality effects in face perception..- Hemispheric asymmetries in face recognition and naming: effects of prior stimulus exposure..- Patterns of cerebral dominance in wholistic and featural stages of facial processing..- Hemispheric differences in the evoked potential to face stimuli..- Cerebral and behavioural asymmetries in the processing of "unusual" faces: A review..- 7. Prosopagnosias.- Current issues on prosopagnosia..- The cognitive psychophysiology of prosopagnosia..- Prosopagnosia: Anatomic and physiologic aspects..- Faces and non-faces in prosopagnosic patients..- Observations on a case of prosopagnosia..- 8. Brain Pathology.- Facial processing in the dementias..- The matching of famous and unknown faces, given either the internal or the external features: A study on patients with unilateral brain lesions..- Face recognition dysfunction and delusional mis identification syndromes (D.M.S.)..- 9. Facial Expressions.- Facial expression processing..- The perception of action versus feeling in facial expression..- Towards the quantification of facial expressions with the use of a mathematic model of the face..- Is the faster processing of expressions of happiness modality-specific?.- Primary stages in single-glance face recognition: Expression and identity..- Affective and cognitive decisions on faces in normals..- 10. Applications and Computer Technology.- Dynamics of facial recall..- The recall and reconstruction of faces: Implications for theory and practice..- An interactive computer system for retrieving faces..- Investigating face recognition with an image processing computer..- Practical face recognition and verification with WISARD..- 11. An Overview.- Plenary session. An overview. Complementary approaches to common problems in face recognition..- 12. References.- Addresses of Principal Authors.

Exploring recognition with interchanged facial features

Abstract: Any attempt to unravel the mechanism underlying the process of human face recognition must begin with experiments that explore human sensitivity to differences between a perceived image and an original memory trace. A set of three consecutive experiments are reported that were collectively designed to measure the relative importance of different facial features. The method involved the use of image-processing equipment to interchange cardinal features among frontally viewed target faces. Observers were required to indicate which of the original target faces most resembled the modified faces. The results clearly establish the dominant influence of the head outline as the major recognition feature. Next in importance is the eye/eyebrow combination, followed by the mouth, and then the nose. As a recognition feature in a frontally presented face, the nose is hardly noticed. The number of apparently random responses to some faces indicates that a surprisingly different face can sometimes arise from a fortuitou...

Practical Face Recognition and Verification with Wisard

Abstract: WISARD (Wilkie, Aleksander, and Stonham’s Recognition Device) is a general purpose pattern recognition machine with a special semi-parallel structure unlike that of conventional single instruction single data computers. The machine is self-adapting. It does not require programming where an explict set of rules, defining the operations to be performed on the data, have to be supplied. The behaviour of the system is established by a learning process whereby a representative set of patterns from the class of data to be recognised, is input to the machine. A wide range of pattern recognition problems can be solved with this approach, they include industrial inspection, speech recognition, medical pattern recognition and artificial vision.

Dynamics of Facial Recall

Abstract: The purpose of this chapter is to examine some of the dynamic properties of facial memory, particularly facial recall. Most research to date on face memory has employed recognition as opposed to recall procedures. One reason for this emphasis is that while facial recognition is a frequent, everday activity, facial recall or description of someone is much less common. Hence, the study of facial recognition processes has ecological validity. A second reason is essentially methodological. How does one adequately assess facial memory through recall? Having witnesses provide drawings presents the problem of artistic skills. Having witnesses provide verbal descriptions presents the problem of describing a complex spatial configuration. In both cases, the difficulties at the response level precludes knowing whether the witness possesses more facial information than shown in the resulting external representation.

The mental representation of faces: Spatial and temporal factors

Abstract: Three experiments are reported on the mental representation of faces with respect to the production of a face superiority effect. The effects of varying spatial position of the features and type of accompanying feature were investigated. Variations in attention to facial versus nonfacial features were considered by regression analyses, allowing an assessment of which was the more facelike of any two displays. Such regression analyses may have application to other recognition tasks if attention is divided between aspects of the display. Two further experiments explored the role of exposure duration in face superiority effects. The relationship of the results to models of face recognition is considered.

Spatial contrast sensitivity in facial recognition.

Abstract: Recognition and learning of complex images may depend on spatial processing characteristics of the visual system. Prosopagnosia, an impairment of visual learning and recognition of faces, might result from impaired perception in spatial channels carrying crucial information. We studied spatial contrast sensitivity (SCS) in two subjects with stable facial recognition defects. One had relative SCS reduction for high-frequency gratings but could process high frequencies in room light. The other had normal SCS. Both had intact spatial processing relative to image size. The results suggest that impairments in visual spatial channels are not necessary for the development of prosopagnosia.

Facial expression processing.

Abstract: This paper presents a theoretical analysis of the process of recognising facial expressions. That process is interesting in its own right, since it touches upon the general problems of knowing other minds and of social communication. In addition, analysis of facial expression processing may contribute to understanding face recognition generally.

Development of an Autonomous Face Recognition Machine.

Abstract: : A computer algorithm was developed which successfully locates and identifies human face(s) that are present in a digitized computer image. In the process of finding the facial image, the algorithm simultaneously determines the boundary locations of the sides of the eyes, the center of the face, the tip of the nose and the vertical center of the mouth. Detection of facial images is based on analysis a digitized scene for the presence of characteristic facial feature signatures for the eyes, nose and mouth. These signatures are generated by the application of a 'center of mass' calculation to each pixel row and column for various sub-sections of the digitized scene. The presence of a face is confirmed, and its feature locations are determined based on the presence and location of the local maxima and minima which occur in these curvilinear signatures. Keywords: image processing; artificial intelligence; theses.

Hemispheric Asymmetries in Face Recognition and Naming: Effects of Prior Stimulus Exposure

Abstract: Detailed models of the cognitive operations subserving performance in a number of processing domains such as reading, spelling and object recognition have become increasingly prevalent in both cognitive psychology and neuropsychology. Such models attempt to distinguish between functionally separable components of processing, and to describe their organisation and operation. The study of face recognition has recently been the subject of the development of such models, and in the last three or four years at least four models have been offered (Bruce, 1983; Ellis, 1983; Hay & Young, 1982; Rhodes, 1985). These models are similar in that they distinguish between the following processing components (or stages) in face recognition: 1) visual-spatial processing; 2) a face representational system which permits recognition of known or familiar faces; 3) semantic information; and 4) naming. The precise details of the model differ, however, in many respects and in particular in the characterisation of how the system which permits the recognition of a face as a known or familiar face (without necessarily fully accessing semantic and name information) is assumed to operate. Ellis’ (1983) model posits a “familiarity check”, whereas Bruce’s (1983) model would suggest that the “structural code” would be accessed, and in Hay & Young’s model known faces may be classified as such when they gain access to “face recognition units” which may be seen as being face analogs of input logogens in Morton’s (1979) model of visual word recognition. Finally, Rhodes’ (1985) model would appear to involve the accessing of her posited processing stage labelled “view-independent topographical representation”.

Visual Attention in Brains and Computers

Abstract: : Existing computer programs designed to perform visual recognition of objects suffer from a basic weakness: the inability to spotlight regions in the image that potentially correspond to objects of interest. The brain's mechanisms of visual attention, elucidated by psychophysicists and neurophysiologists, may suggest a solution to the computer's problem of object recognition.