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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 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 symmetiic 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 3 D 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.