News on facial views from humans and machine

Everyday experience suggests that faces can be recognized despite large changes of viewpoint. In this study we used the Inter-Extra-Ortho paradigm from Buelthoff and Edelman (1992) in order to investigate the underlying mechanisms of face recognition. We found systematic effects of viewpoint, which were consistent with computational approaches using interpolation of 2D views. Our results extend the findings from Buelthoff and Edelman on unfamiliar objects to the highly familiar class of faces thus confirming image-based recognition processes independent of class familiarity. In addition, we found that human recognition performance was qualitatively similar to the performance of an extended version of the computational recognition scheme proposed by Wallraven and Buelthoff (2001) using the same faces as in the psychophysical experiments. This algorithm entails processing view-based features and their spatial relations in a dynamic context and is consistent with evidence from psychophysics suggesting separate representations for featural and configural information in face recognition (Schwaninger, Lobmaier, & Collishaw, 2002).