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Preferred Spatial Frequencies for Human Face Processing Are Associated with Optimal Class Discrimination in the Machine

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  • Matthias S Keil
  • Agata Lapedriza
  • David Masip
  • Jordi Vitria

Abstract

Psychophysical studies suggest that humans preferentially use a narrow band of low spatial frequencies for face recognition. Here we asked whether artificial face recognition systems have an improved recognition performance at the same spatial frequencies as humans. To this end, we estimated recognition performance over a large database of face images by computing three discriminability measures: Fisher Linear Discriminant Analysis, Non-Parametric Discriminant Analysis, and Mutual Information. In order to address frequency dependence, discriminabilities were measured as a function of (filtered) image size. All three measures revealed a maximum at the same image sizes, where the spatial frequency content corresponds to the psychophysical found frequencies. Our results therefore support the notion that the critical band of spatial frequencies for face recognition in humans and machines follows from inherent properties of face images, and that the use of these frequencies is associated with optimal face recognition performance.

Suggested Citation

  • Matthias S Keil & Agata Lapedriza & David Masip & Jordi Vitria, 2008. "Preferred Spatial Frequencies for Human Face Processing Are Associated with Optimal Class Discrimination in the Machine," PLOS ONE, Public Library of Science, vol. 3(7), pages 1-5, July.
    • Handle: RePEc:plo:pone00:0002590
    DOI: 10.1371/journal.pone.0002590
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    1. Toshihiko Hosoya & Stephen A. Baccus & Markus Meister, 2005. "Dynamic predictive coding by the retina," Nature, Nature, vol. 436(7047), pages 71-77, July.
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    Cited by:

    1. Matthias S Keil, 2009. "“I Look in Your Eyes, Honey”: Internal Face Features Induce Spatial Frequency Preference for Human Face Processing," PLOS Computational Biology, Public Library of Science, vol. 5(3), pages 1-13, March.

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