Sparse Representation Based Classification for Face Recognition by k-LiMapS Algorithm

  • Alessandro Adamo
  • Giuliano Grossi
  • Raffaella Lanzarotti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7340)

Abstract

In this paper, we present a new approach for face recognition that is robust against both poorly defined and poorly aligned training and testing data even with few training samples. Working in the conventional feature space yielded by the Fisher’s Linear Discriminant analysis, it uses a recent algorithm for sparse representation, namely k-LiMapS, as general classification criterion. Such a technique performs a local ℓ0 pseudo-norm minimization by iterating suitable parametric nonlinear mappings. Thanks to its particular search strategy, it is very fast and able to discriminate among separated classes lying in the low-dimension Fisherspace. Experiments are carried out on the FRGC version 2.0 database showing good classification capability even when compared with the state-of-the-art ℓ1 norm-based sparse representation classifier (SRC).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Alessandro Adamo
    • 1
  • Giuliano Grossi
    • 2
  • Raffaella Lanzarotti
    • 2
  1. 1.Dipartimento di MatematicaUniversità degli Studi di MilanoMilanoItaly
  2. 2.Dipartimento di Scienze dell’InformazioneUniversità degli Studi di MilanoMilanoItaly

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