Open Access

Localized versus Locality-Preserving Subspace Projections for Face Recognition

EURASIP Journal on Image and Video Processing20072007:017173

DOI: 10.1155/2007/17173

Received: 1 May 2006

Accepted: 26 March 2007

Published: 13 May 2007

Abstract

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.

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Authors’ Affiliations

(1)
Faculty of Electronics and Telecommunications, “Gh. Asachi” Technical University of Iaşi
(2)
Faculty of Medical Bioengineering, “Gr. T. Popa” University of Medicine and Pharmacy
(3)
Institute for Theoretical Computer Science, Romanian Academy

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Copyright

© 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.