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  Learning from Humans: Computational Modeling of Face Recognition

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

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Wallraven, C1, 2, Author           
Schwaninger, A1, 2, Author           
Bülthoff, HH1, 2, Author           
Affiliations:
1Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497797              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 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.

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 Dates: 2005-12
 Publication Status: Issued
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 Rev. Type: -
 Identifiers: DOI: 10.1080/09548980500508844
BibTex Citekey: 3776
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Title: Network: Computation in Neural Systems
  Other : Netw.-Comput. Neural Syst.
Source Genre: Journal
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Publ. Info: Bristol : IOP Pub.
Pages: - Volume / Issue: 16 (4) Sequence Number: - Start / End Page: 401 - 418 Identifier: ISSN: 0954-898X
CoNE: https://pure.mpg.de/cone/journals/resource/954925576018