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Incremental Principal Component Analysis-Based Sparse Representation for Face Pose Classification

  • Yuyao Zhang
  • Y. Benhamza
  • Khalid Idrissi
  • Christophe Garcia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8192)

Abstract

This paper proposes an Adaptive Sparse Representation pose Classification (ASRC) algorithm to deal with face pose estimation in occlusion, bad illumination and low-resolution cases. The proposed approach classifies different poses, the appearance of face images from the same pose being modelled by an online eigenspace which is built via Incremental Principal Component Analysis. Then the combination of the eigenspaces of all pose classes are used as an over-complete dictionary for sparse representation and classification. However, the big amount of training images may lead to build an extremely large dictionary which will decelerate the classification procedure. To avoid this situation, we devise a conditional update method that updates the training eigenspace only with the misclassified face images. Experimental results show that the proposed method is very robust when the illumination condition changes very dynamically and image resolutions are quite poor.

Keywords

Sparse Representation Pose Classification Incremental Principal Component Analysis 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yuyao Zhang
    • 1
  • Y. Benhamza
    • 2
  • Khalid Idrissi
    • 1
  • Christophe Garcia
    • 1
  1. 1.CNRS INSA-Lyon, LIRIS, UMR CNRS 5205University de LyonFrance
  2. 2.Laboratoire LARATICINTTIC Oran AlgrieAlgeria

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