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Localized versus Locality-Preserving Subspace Projections for Face Recognition

Author(s): Iulian B. Ciocoiu | Hariton N. Costin

Journal: EURASIP Journal on Image and Video Processing
ISSN 1687-5176

Volume: 2007;
Date: 2007;
Original page

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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