June 2004
Volume 4, Issue 8
Free
Vision Sciences Society Annual Meeting Abstract  |   August 2004
Physiologically inspired neural model for the prototype-referenced encoding of faces
Author Affiliations
  • Martin A. Giese
    ARL, Dept. of Cognitive Neurology, Univ. Clinic Tuebingen, Germany
  • Rodrigo Sigala
    ARL, Dept. of Cognitive Neurology, Univ. Clinic Tuebingen, Germany
  • Christian Wallraven
    Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
  • David A. Leopold
    Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
Journal of Vision August 2004, Vol.4, 213. doi:10.1167/4.8.213
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Martin A. Giese, Rodrigo Sigala, Christian Wallraven, David A. Leopold; Physiologically inspired neural model for the prototype-referenced encoding of faces. Journal of Vision 2004;4(8):213. doi: 10.1167/4.8.213.

      Download citation file:


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

      ×
  • Supplements
Abstract

Some psychological models for face recognition assume that faces are encoded as vectors in face spaces relative to an average face, or face prototype [T Valentine, Q J Exp Psychol A, 43, 161 (1991)]. So far it has been largely unclear how such a prototype-referenced encoding can be realized at a neural level. Recent electrophysiological data supports the relevance of such encoding in monkey visual cortex. Neurons in area IT, after training with human faces, show monotonic tuning with respect to the caricature level of face stimuli [D Leopold et al., Soc. of Neurosci., Poster 590.7 (2003)]. A neural model is presented that accounts for these electrophysiological results. The model consists of a hierarchy of layers with physiologically plausible neural feature detectors. The complexity of the extracted features increases along the hierarchy. Neurons on the highest level encode example views of faces. The tuning of these neurons is determined by the difference between the feature vector representing the test face, and an average feature vector that is computed from the previous history of stimulation. The neurons are tuned monotonically with respect to the length of the difference vector, and show angular tuning with respect to its direction in feature space. The model was tested with gray-level images generated with a morphable 3D face model [V Blanz, T Vetter, SIGGRAPH '99, 187–194 (1999)], replicating the stimulus set from the electrophysiological study. We conclude that prototype-referenced encoding, compared with the encoding in shape spaces with absolute coordinates, increases coding efficiency by optimally exploiting the available neural hardware.

Giese, M. A., Sigala, R., Wallraven, C., Leopold, D. A.(2004). Physiologically inspired neural model for the prototype-referenced encoding of faces [Abstract]. Journal of Vision, 4( 8): 213, 213a, http://journalofvision.org/4/8/213/, doi:10.1167/4.8.213. [CrossRef]
Footnotes
 Supported by the Deutsche Volkswagenstiftung, Hermann and Lilly Schilling Foundation, and the Max Planck Society.
×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×