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Conference Paper

Learning from humans: computational modeling of face recognition

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

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

/persons/resource/persons83839

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

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pdf2272.pdf
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Citation

Wallraven, C., Schwaninger, A., & Bülthoff, H. (2004). Learning from humans: computational modeling of face recognition. In Early Cognitive Vision Workshop (ECOVISION 2004) (pp. 1-4).


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-D8EB-3
Abstract
In this paper we propose a computational architecture of face recognition based on evidence from cognitive research. Using an implementation of this architecture we were able to model aspects of human performance, which were found in psychophysical studies. Furthermore, results from additional recognition experi ments show that our framework is able to achieve excellent recognition performance even under large view rotations. Thus, our study is an example of how results from cognitive research can be used to construct recognition systems with better performance. Finally, our results also make new experimental predictions, which can be tested in further psychophysical studies thus closing the loop between experiment and modeling.