Authors
Shimon Edelman
Cornell University
Abstract
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.
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References found in this work BETA

Looking at Upside-Down Faces.Robert K. Yin - 1969 - Journal of Experimental Psychology 81 (1):141.
Why Faces Are and Are Not Special: An Effect of Expertise.Rhea Diamond & Susan Carey - 1986 - Journal of Experimental Psychology: General 115 (2):107-117.

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