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Robust Coarse-to-Fine Sparse Representation for Face Recognition

  • Yunlian Sun
  • Massimo Tistarelli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8157)

Abstract

Recently Sparse Representation-based classification (SRC) has been successfully applied to pattern classification. In this paper, we present a robust Coarse-to-Fine Sparse Representation (CFSR) for face recognition. In the coarse coding phase, the test sample is represented as a linear combination of all the training samples. In the last phase, a number of “nearest neighbors” is determined to represent the test sample to perform classification. CFSR produces the sparseness through the coarse phase, and exploits the local data structure to perform classification in the fine phase. Moreover, this method can make a better classification decision by determining an individual dictionary for each test sample. Extensive experiments on benchmark face databases show that our method has competitive performance in face recognition compared with other state-of-the-art methods.

Keywords

coarse-to-fine sparse representation face recognition 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yunlian Sun
    • 1
  • Massimo Tistarelli
    • 1
  1. 1.Department of Sciences and Information TechnologyUniveristy of SassariSassariItaly

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