Experiments on Lattice Independent Component Analysis for Face Recognition

  • Ion Marqués
  • Manuel Graña
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6687)


In previous works we have proposed Lattice Independent Component Analysis (LICA) for a variety of image processing tasks. The first step of LICA is to identify strong lattice independent components from the data. The set of strong lattice independent vector are used for linear unmixing of the data, obtaining a vector of abundance coefficients. In this paper we propose to use the resulting abundance values as features for clasification, specifically for face recognition. We report results on two well known benchmark databases.


Principal Component Analysis Face Recognition Linear Discriminant Analysis Hyperspectral Image Locality Preserve Projection 
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 2011

Authors and Affiliations

  • Ion Marqués
    • 1
  • Manuel Graña
    • 1
  1. 1.Computational Intelligence Group, Dept. CCIAUPV/EHUSan SebastianSpain

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