We consider the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise. We cast the recognition problem as one of classifying among multiple linear regression models and argue that new theory from sparse signal representation offers the key to addressing this problem. Based on a sparse representation computed by C1-minimization, we propose a general classification algorithm for (image-based) object recognition. This new framework provides new insights into two crucial issues in face recognition: feature extraction and robustness to occlusion. For feature extraction, we show that if sparsity in the recognition problem is properly harnessed, the choice of features is no longer critical. What is critical, however, is whether the number of features is sufficiently large and whether the sparse representation is correctly computed. Unconventional features such as downsampled images and random projections perform just as well as conventional features such as eigenfaces and Laplacianfaces, as long as the dimension of the feature space surpasses certain threshold, predicted by the theory of sparse representation. This framework can handle errors due to occlusion and corruption uniformly by exploiting the fact that these errors are often sparse with respect to the standard (pixel) basis. The theory of sparse representation helps predict how much occlusion the recognition algorithm can handle and how to choose the training images to maximize robustness to occlusion. We conduct extensive experiments on publicly available databases to verify the efficacy of the proposed algorithm and corroborate the above claims.

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Robust Face Recognition via Sparse Representation

As one of the most successful applications of image analysis and understanding, face recognition has recently received significant attention, especially during the past several years. At least two reasons account for this trend: the first is the wide range of commercial and law enforcement applications, and the second is the availability of feasible technologies after 30 years of research. Even though current machine recognition systems have reached a certain level of maturity, their success is limited by the conditions imposed by many real applications. For example, recognition of face images acquired in an outdoor environment with changes in illumination and/or pose remains a largely unsolved problem. In other words, current systems are still far away from the capability of the human perception system.This paper provides an up-to-date critical survey of still- and video-based face recognition research. There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date review of the existing literature, and the second is to offer some insights into the studies of machine recognition of faces. To provide a comprehensive survey, we not only categorize existing recognition techniques but also present detailed descriptions of representative methods within each category. In addition, relevant topics such as psychophysical studies, system evaluation, and issues of illumination and pose variation are covered.

Face recognition: A literature survey

Part I. Experimental Studies: 2. Experiment in psychology 3. Experiments on perceiving III Experiments on imaging 4-8. Experiments on remembering: (a) The method of description (b) The method of repeated reproduction (c) The method of picture writing (d) The method of serial reproduction (e) The method of serial reproduction picture material 9. Perceiving, recognizing, remembering 10. A theory of remembering 11. Images and their functions 12. Meaning Part II. Remembering as a Study in Social Psychology: 13. Social psychology 14. Social psychology and the matter of recall 15. Social psychology and the manner of recall 16. Conventionalism 17. The notion of a collective unconscious 18. The basis of social recall 19. A summary and some conclusions.

Remembering. A Study in Experimental and Social Psychology, Cambridge (University Press) 1964.

An approach to the detection and identification of human faces is presented, and a working, near-real-time face recognition system which tracks a subject's head and then recognizes the person by comparing characteristics of the face to those of known individuals is described. This approach treats face recognition as a two-dimensional recognition problem, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. Face images are projected onto a feature space ('face space') that best encodes the variation among known face images. The face space is defined by the 'eigenfaces', which are the eigenvectors of the set of faces; they do not necessarily correspond to isolated features such as eyes, ears, and noses. The framework provides the ability to learn to recognize new faces in an unsupervised manner. >

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Face recognition using eigenfaces

We propose that the crucial difference between human cognition and that of other species is the ability to participate with others in collaborative activities with shared goals and intentions: shared intentionality. Participation in such activities requires not only especially powerful forms of intention reading and cultural learning, but also a unique motivation to share psychological states with oth- ers and unique forms of cognitive representation for doing so. The result of participating in these activities is species-unique forms of cultural cognition and evolution, enabling everything from the creation and use of linguistic symbols to the construction of social norms and individual beliefs to the establishment of social institutions. In support of this proposal we argue and present evidence that great apes (and some children with autism) understand the basics of intentional action, but they still do not participate in activities involving joint intentions and attention (shared intentionality). Human children's skills of shared intentionality develop gradually during the first 14 months of life as two ontogenetic pathways intertwine: (1) the general ape line of understanding others as animate, goal-directed, and intentional agents; and (2) a species-unique motivation to share emotions, experience, and activities with other persons. The develop- mental outcome is children's ability to construct dialogic cognitive representations, which enable them to participate in earnest in the collectivity that is human cognition.

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Understanding and sharing intentions: The origins of cultural cognition

Introduction. This study was designed to elucidate the relationship between different types of covert face recognition. Some patients with prosopagnosia (i.e., the profound inability to recognise previously familiar faces) nonetheless evince autonomic face recognition (elevated skin-conductance levels to familiar faces) or behavioural indices of covert recognition (i.e., priming; interference effects; matching effects; face-name learning). One prosopagnosic patient revealed both autonomic and behavioural covert face recognition-which suggests they may arise from some common basis. Method. To test this claim a patient with the Capgras delusion (i.e., holding the belief that others have been replaced by impostors, etc.) was tested on each type of covert face recognition and her results compared with agematched controls. We know that the Capgras delusion is characterised by good overt or conscious face recognition coupled with the absence of autonomic discrimination between familiar and unfamiliar faces. The question addressed here was whether, compared with age- and gender-matched controls, the patient, B.P., would show neither autonomic nor behavioural covert face recognition. Results. The answer was that, although she showed no autonomic discrimination, her performance on a priming task and a test of face/name interference were normal. The controls, as expected, revealed covert face recognition on both the autonomic and behavioural measures. Conclusions. The results imply in B.P. a clear dissociation between autonomic and behavioural measures of covert face recognition. The theoretical implications of these results are discussed.

Automatic without autonomic responses to familiar faces : Differential components of covert face recognition in a case of Capgras delusion

Background: Delusions involving the internet have been reported as examples of the influence of cultural innovations on delusion formation, although there has been some debate as to

'Internet delusions': a case series and theoretical integration.

Scientists strive to build systems than can detect and recognize faces. One such system already exists: the human brain. How this system operates, however, is far from being fully understood. In this article, we review the psychological evidence regarding the process of face detection in particular. Evidence is collated from a variety of face-processing tasks including stimulus detection; face categorization, visual search, first saccade analysis, and face detection itself. Together, the evidence points towards a multistage-processing model of face detection. These stages involve preattentive processing, template fitting, and template evaluation. Comparisons are made with automatic face-detection systems. © 2003 Wiley Periodicals, Inc. Int J Imaging Syst Technol 13, 3–7, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.10040

Hadyn D. Ellis

Papers

Automatic without autonomic responses to familiar faces : Differential components of covert face recognition in a case of Capgras delusion

'Internet delusions': a case series and theoretical integration.

How we detect a face: A survey of psychological evidence

The development of face recognition

Effect of stimulus orientation upon haptic perception of the horizontal-vertical illusion.