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Localized versus Locality-Preserving Subspace Projections for Face Recognition

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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Correspondence to IulianB Ciocoiu.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Ciocoiu, I., Costin, H. Localized versus Locality-Preserving Subspace Projections for Face Recognition. J Image Video Proc 2007, 017173 (2007). https://doi.org/10.1155/2007/17173

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Keywords

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