October 2003
Volume 3, Issue 9
Free
Vision Sciences Society Annual Meeting Abstract  |   October 2003
News on facial views from humans and machine
Author Affiliations
  • Adrian Schwaninger
    Max Planck Institute for Biological Cybernetics, Germany
  • Christian Wallraven
    Max Planck Institute for Biological Cybernetics, Germany
  • Sandra Schuhmacher
    Dept. of Psychology, University of Zurich, Switzerland
  • Heinrich H Buelthoff
    Max Planck Institute for Biological Cybernetics, Germany
Journal of Vision October 2003, Vol.3, 837. doi:10.1167/3.9.837
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      Adrian Schwaninger, Christian Wallraven, Sandra Schuhmacher, Heinrich H Buelthoff; News on facial views from humans and machine. Journal of Vision 2003;3(9):837. doi: 10.1167/3.9.837.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

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).

Schwaninger, A., Wallraven, C., Schuhmacher, S., Buelthoff, H. H.(2003). News on facial views from humans and machine [Abstract]. Journal of Vision, 3( 9): 837, 837a, http://journalofvision.org/3/9/837/, doi:10.1167/3.9.837. [CrossRef]
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