Proceedings of the Annual Meeting of the Cognitive Science Society

Face recognition stands out as a singular case of object recognition: Although most faces are very much alike, people discriminate between many different faces with outstanding efficiency. Even though little is known about the mechanisms of face recognition, viewpoint dependence — a recurrent characteristic of research in face recognition — could help to understand algorithmic and representational issues. The current research tests whether learning only one view of a face could be sufficient to generalize recognition to other views of the same face. Computational and psychophysical research (Poggio & Vetter, 1992) showed that learning one view of a bilaterally symmetric object could be sufficient for its recognition, if this view allows the computation of a symmetric, "virtual," view. Faces are roughly bilaterally symmetric objects. Learning a side-view — which always has a symmetric view — should allow for better generalization performances than learning the frontal view. Two psychophysical experiments tested these predictions. Stimuli were views of shaded 3D models of laserscanned faces. The first experiment tested whether a particular view of a face was canonical. The second experiment tested which single views of a face give rise to best generalization performances. The results were compatible with the theoretical predictions of Poggio and Vetter (1992): learning a side view allows better generalization performances than learning the frontal view.