de.mpg.escidoc.pubman.appbase.FacesBean
Deutsch
 
Hilfe Wegweiser Impressum Kontakt Einloggen
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Vortrag

Computational modeling of face recognition

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84298

Wallraven,  C
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84420

Schwaninger,  A
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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;

Externe Ressourcen
Es sind keine Externen Ressourcen verfügbar
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Wallraven, C., Schwaninger, A., & Bülthoff, H. (2003). Computational modeling of face recognition. Talk presented at 44th Annual Meeting of The Psychonomic Society. Vancouver, Canada.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-DB0F-E
Zusammenfassung
Recent psychophysical results on face recognition (Schwaninger et al., 2002) support the notion that processing of faces relies on two separate routes. The first route processes highdetail components of the face (such as eyes, mouth, etc.), whereas the second route processes the configural relationship between these components. This model was successfully used to explain several aspects of face recognition, such as the Thatcher Illusion or the stimuli composed by Young et al. (1987). We discuss a computational framework, in which we implemented configural and component processing using image fragments and their spatial layout. Using the stimuli from the original psychophysical study, we were able to model the recognition performance. In addition, large-scale tests with highly realistic computer-rendered faces from the MPI database show better performance and robustness than do other computational approaches using one processing route only.