This article explores the concept of recognizing a face holistically and examines the experimental paradigms that serve as the “gold standards” for holistic perception. It discusses the contribution of featural and configural information to the holistic process and the controversy surrounding these often misunderstood concepts. It claims that the recruitment of holistic processes is what distinguishes faces from most types of object recognition. The discussion focuses on the kind of featural and configural information that is impaired in an inverted face (...) as well as the information that is spared. Finally, the article considers the concept of a “perceptual field” that allows for the preservation of both featural and configural information within a restricted spatial region of an inverted face. (shrink)

The statistical structure of a class of objects such as human faces can be exploited to recognize familiar faces from novel viewpoints and under variable illumination conditions. We present computational and psychophysical data concerning the extent to which class-based learning transfers or generalizes within the class of faces. We rst examine the computational prerequisite for generalization across views of novel faces, namely, the similarity of di erent faces to each other. We next describe two computational models which exploit the similarity (...) structure of the class of faces. The performance of these models constrains hypotheses about the nature of face representation in human vision, and supports the notion that human face processing operates in a class-based fashion. Finally, we relate the computational data to well-established ndings in the human memory literature concerning the relationship between the typicality and recognizability of faces. (shrink)

The use of computational models for understanding human face perception and recognition has a long and intriguing history that runs parallel to efforts in the engineering literature to develop algorithms for computer-based face recognition systems. This article considers the insights gained from combining computational and cognitive approaches to the study of human face recognition and discusses the ways in which computational models have informed studies of human face processing and vice versa. It explains the concept of a face space, in (...) its abstract psychological, and physical/computational forms. The study shows how adaptation as a method is beginning to reveal properties of neural representations in a way that connects with the cognitive/perceptual approach. Finally, it discusses recent progress in state-of-the-art computational models of face recognition, which offers a new perspective on the cognitive-computational dialog. (shrink)

Research in face recognition has largely been divided between those projects concerned with front‐end image processing and those projects concerned with memory for familiar people. These perceptual and cognitive programmes of research have proceeded in parallel, with only limited mutual influence. In this paper we present a model of human face recognition which combines both a perceptual and a cognitive component. The perceptual front‐end is based on principal components analysis of face images, and the cognitive back‐end is based on a (...) simple interactive activation and competition architecture. We demonstrate that this model has a much wider predictive range than either perceptual or cognitive models alone, and we show that this type of combination is necessary in order to analyse some important effects in human face recognition. In sum, the model takes varying images of “known” faces and delivers information about these people. (shrink)