Abstract

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.