English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Physiologically inspired neural model for the prototype-referenced encoding of faces

Giese, M., Sigala, R., Wallraven, C., & Leopold, D. (2004). Physiologically inspired neural model for the prototype-referenced encoding of faces. Poster presented at Fourth Annual Meeting of the Vision Sciences Society (VSS 2004), Sarasota, FL, USA.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-D859-D Version Permalink: http://hdl.handle.net/21.11116/0000-0005-62B8-D
Genre: Poster

Files

show Files

Locators

show
hide
Description:
-

Creators

show
hide
 Creators:
Giese, MA, Author              
Sigala, R, Author              
Wallraven, C1, 2, Author              
Leopold, D1, 3, Author              
Affiliations:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
2Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497797              
3Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              

Content

show
hide
Free keywords: -
 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.

Details

show
hide
Language(s):
 Dates: 2004-08
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1167/4.8.213
BibTex Citekey: 5543
 Degree: -

Event

show
hide
Title: Fourth Annual Meeting of the Vision Sciences Society (VSS 2004)
Place of Event: Sarasota, FL, USA
Start-/End Date: 2004-04-30 - 2004-05-05

Legal Case

show

Project information

show

Source 1

show
hide
Title: Journal of Vision
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Charlottesville, VA : Scholar One, Inc.
Pages: - Volume / Issue: 4 (8) Sequence Number: - Start / End Page: 213 Identifier: ISSN: 1534-7362
CoNE: https://pure.mpg.de/cone/journals/resource/111061245811050