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Recognizing faces by weakly orthogonalizing against perturbations

  • Kenji Nagao
  • Masaki Sohma
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1407)

Abstract

In this paper, we address the problem of face recognition under drastic changes of the imaging processes through which the facial images are acquired. A new method is proposed. Unlike the conventional algorithms that use only the face features, the present method exploits the statistical information of the variations between the face image sets being compared, in addition to the features of the faces themselves. To incorporate the face and perturbation features for recognition, a technique called weak orthogonalization of the two subspaces has been developed that transforms the two overlapped subspaces such that the volume of the intersection of the resulting two subspaces is minimized. Matching is performed in the transformed face space that has thus been weakly orthogonalized against perturbation space. Results using real pictures of the frontal faces from drivers' licenses demonstrate the effectiveness of the new algorithm.

Keywords

Face Recognition Face Image Face Space Face Recognition Task Correct Recognition Rate 
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 1998

Authors and Affiliations

  • Kenji Nagao
    • 1
  • Masaki Sohma
    • 1
  1. 1.Matsushita Research Institute TokyoKawasakiJapan

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