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  • Research Article
  • Open Access

Localized versus Locality-Preserving Subspace Projections for Face Recognition

EURASIP Journal on Image and Video Processing20072007:017173

  • Received: 1 May 2006
  • Accepted: 26 March 2007
  • Published:


Three different localized representation methods and a manifold learning approach to face recognition are compared in terms of recognition accuracy. The techniques under investigation are (a) local nonnegative matrix factorization (LNMF); (b) independent component analysis (ICA); (c) NMF with sparse constraints (NMFsc); (d) locality-preserving projections (Laplacian faces). A systematic comparative analysis is conducted in terms of distance metric used, number of selected features, and sources of variability on AR and Olivetti face databases. Results indicate that the relative ranking of the methods is highly task-dependent, and the performances vary significantly upon the distance metric used.


  • Manifold
  • Image Processing
  • Pattern Recognition
  • Computer Vision
  • Face Recognition


Authors’ Affiliations

Faculty of Electronics and Telecommunications, “Gh. Asachi” Technical University of Iaşi, Iaşi, 700506, Romania
Faculty of Medical Bioengineering, “Gr. T. Popa” University of Medicine and Pharmacy, Iaşi, 700115, Romania
Institute for Theoretical Computer Science, Romanian Academy, Iaşi Branch, Iaşi, 700506, Romania


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© I. B. Ciocoiu and H. N. Costin. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.