Item

Abstract

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 many research on faces, could inform algorithms and representations. Poggio and Vetter‘s symmetry argument [10] predicts that learning only one view of a face may be sufficient for recognition, if this view allows the computation of a symmetric, virtual, view. More specifically, as faces are roughly bilaterally symmetric objects, learning a side-view - which always has a symmetric view - should give rise to better generalization performances than learning the frontal view. It is also predicted that among all new views, a virtual view should be best recognized. We ran two psychophysical experiments to test these predictions. Stimuli were views of 3D models of laser-scanned faces. Only shape was available for recognition; all other face cues - texture, color, hair, etc. - were removed from the stimuli. 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 symmetry argument: face recognition from a single view is always better when the learned view allows the computation of a symmetric view.