Learning from humans: Computational modeling of face recognition

Christian Wallraven, Adrian Schwaninger, Heinrich H. Bülthoff

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


In this paper, we propose a computational architecture of face recognition based on evidence from cognitive research. 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 modeling.

Original languageEnglish
Pages (from-to)401-418
Number of pages18
JournalNetwork: Computation in Neural Systems
Issue number4
Publication statusPublished - 2005 Dec


  • Configural and component information
  • Face recognition
  • Local features

ASJC Scopus subject areas

  • Neuroscience (miscellaneous)


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