de.mpg.escidoc.pubman.appbase.FacesBean
English
 
Help Guide Disclaimer Contact us Login
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Report

Conditions for viewpoint dependent face recognition

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83839

Bülthoff,  HH
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

Locator
There are no locators available
Fulltext (public)
There are no public fulltexts available
Supplementary Material (public)
There is no public supplementary material available
Citation

Schyns, P., & Bülthoff, H.(1993). Conditions for viewpoint dependent face recognition (AIM-1432).


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-ED8E-3
Abstract
Face recognition stands out as a singular case of object recognition: although most faces are very much alike, people discriminate between many different faces with outstanding efficiency. Even though little is known about the mechanisms of face recognition, viewpoint dependence, a recurrent characteristic of many research on faces, could inform algorithms and representations. Poggio and Vetter‘s symmetry argument [10] predicts that learning only one view of a face may be sufficient for recognition, if this view allows the computation of a symmetric, virtual, view. More specifically, as faces are roughly bilaterally symmetric objects, learning a side-view - which always has a symmetric view - should give rise to better generalization performances than learning the frontal view. It is also predicted that among all new views, a virtual view should be best recognized. We ran two psychophysical experiments to test these predictions. Stimuli were views of 3D models of laser-scanned faces. Only shape was available for recognition; all other face cues - texture, color, hair, etc. - were removed from the stimuli. The first experiment tested whether a particular view of a face was canonical. The second experiment tested which single views of a face give rise to best generalization performances. The results were compatible with the symmetry argument: face recognition from a single view is always better when the learned view allows the computation of a symmetric view.