Soft Computing

, Volume 16, Issue 9, pp 1525–1537 | Cite as

Face recognition with lattice independent component analysis and extreme learning machines

  • Ion MarquesEmail author
  • Manuel Graña


We focus on two aspects of the face recognition, feature extraction and classification. We propose a two component system, introducing Lattice Independent Component Analysis (LICA) for feature extraction and Extreme Learning Machines (ELM) for classification. In previous works we have proposed LICA for a variety of image processing tasks. The first step of LICA is to identify strong lattice independent components from the data. In the second step, the set of strong lattice independent vector are used for linear unmixing of the data, obtaining a vector of abundance coefficients. The resulting abundance values are used as features for classification, specifically for face recognition. Extreme Learning Machines are accurate and fast-learning innovative classification methods based on the random generation of the input-to-hidden-units weights followed by the resolution of the linear equations to obtain the hidden-to-output weights. The LICA-ELM system has been tested against state-of-the-art feature extraction methods and classifiers, outperforming them when performing cross-validation on four large unbalanced face databases.


Extreme learning machine (ELM) Face recognition Lattice independent component analysis (LICA) Lattice computing 



I. Marques acknowledges the grant received from the Basque Government through the Research Staff Training Program of the Education, University and Research Department.


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Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.CCIA DepartmentUPV EHU, Computational Intelligence GroupSan SebastianSpain

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