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Color Constancy Algorithms for Object and Face Recognition

  • Christopher Kanan
  • Arturo Flores
  • Garrison W. Cottrell
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6453)

Abstract

Brightness and color constancy is a fundamental problem faced in computer vision and by our own visual system. We easily recognize objects despite changes in illumination, but without a mechanism to cope with this, many object and face recognition systems perform poorly. In this paper we compare approaches in computer vision and computational neuroscience for inducing brightness and color constancy based on their ability to improve recognition. We analyze the relative performance of the algorithms on the AR face and ALOI datasets using both a SIFT-based recognition system and a simple pixel-based approach. Quantitative results demonstrate that color constancy methods can significantly improve classification accuracy. We also evaluate the approaches on the Caltech-101 dataset to determine how these algorithms affect performance under relatively normal illumination conditions.

Keywords

Face Recognition Color Space Color Constancy Sift Descriptor Histogram Normalization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Christopher Kanan
    • 1
  • Arturo Flores
    • 1
  • Garrison W. Cottrell
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of California San DiegoLa JollaUSA

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