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Combining Illumination Normalization Methods for Better Face Recognition

  • Bas Boom
  • Qian Tao
  • Luuk Spreeuwers
  • Raymond Veldhuis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)

Abstract

Face Recognition under uncontrolled illumination conditions is partly an unsolved problem. There are two categories of illumination normalization methods. The first category performs a local preprocessing, where they correct a pixel value based on a local neighborhood in the images. The second category performs a global preprocessing step, where the illumination conditions and the face shape of the entire image are estimated. We use two illumination normalization methods from both categories, namely Local Binary Patterns and Model-based Face Illumination Correction. The preprocessed face images of both methods are individually classified with a face recognition algorithm which gives us two similarity scores for a face image. We combine the similarity scores using score-level fusion, decision-level fusion and hybrid fusion. In our previous work, we show that combining the similarity score of different methods using fusion can improve the performance of biometric systems. We achieved a significant performance improvement in comparison with the individual methods.

Keywords

Face Recognition Face Image Local Binary Pattern Light Direction Face Shape 
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 2009

Authors and Affiliations

  • Bas Boom
    • 1
  • Qian Tao
    • 1
  • Luuk Spreeuwers
    • 1
  • Raymond Veldhuis
    • 1
  1. 1.EEMSC, Signals & SystemsUniversity of TwenteEnschedeThe Netherlands

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