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Learning from Humans: Computational Modeling of Face Recognition

MPS-Authors
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;

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Citation

Wallraven, C., Schwaninger, A., & Bülthoff, H. (2005). Learning from Humans: Computational Modeling of Face Recognition. Network, 16(4), 401-418. doi:10.1080/09548980500508844.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-D369-4
Abstract
In this paper we propose a computational architecture of face recognition based on evidence from cognitive research. Specifically, several recent psychophysical experiments have shown that humans process faces by a combination of configural and component information. Using an appearance-based implementation of this architecture based on low-level features and their spatial relations we were able to model aspects of human performance found in psychophysical studies. Furthermore, results from additional computational recognition experiments show that our framework is able to achieve excellent recognition performance even under large view rotations. Our interdisciplinary study is an example of how results from cognitive research can be used to construct recognition systems with increased performance. Finally, our modeling results also make new experimental predictions that will be tested in further psychophysical studies thus effectively closing the loop between psychophysical experimentation and computational m odeling.