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Proceedings Paper

Selective locality preserving projections for face recognition
Author(s): F. Dornaika; A. Assoum
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Paper Abstract

Recently, a graph-based method was proposed for Linear Dimensionality Reduction (LDR). It is based on Locality Preserving Projections (LPP). LPP is a typical linear graph-based dimensionality reduction (DR) method that has been successfully applied in many practical problems such as face recognition. LPP is essentially a linearized version of Laplacian Eigenmaps. When dealing with face recognition problems, LPP is preceded by a Principal Component Analysis (PCA) step in order to avoid possible singularities. Both PCA and LPP are computed by solving an eigen decomposition problem. In this paper, we propose a novel approach called "Selective Locality Preserving Projections" that performs an eigenvector selection associated with LPP. Consequently, the problem of dimension estimation for LPP is solved. Moreover, we propose a selective approach that performs eigenvector selection for the case where the mapped samples are formed by concatenating the output of PCA and LPP. We have tested our proposed approaches on several public face data sets. Experiments on ORL, UMIST, and YALE Face Databases show significant performance improvements in recognition over the classical LPP. The proposed approach lends itself nicely to many biometric applications.

Paper Details

Date Published: 24 January 2011
PDF: 10 pages
Proc. SPIE 7878, Intelligent Robots and Computer Vision XXVIII: Algorithms and Techniques, 78780Y (24 January 2011); doi: 10.1117/12.876673
Show Author Affiliations
F. Dornaika, Univ. of the Basque Country (Spain)
Ikerbasque, Basque Foundation for Science (Spain)
A. Assoum, Lebanese Univ. (Lebanon)


Published in SPIE Proceedings Vol. 7878:
Intelligent Robots and Computer Vision XXVIII: Algorithms and Techniques
Juha Röning; David P. Casasent; Ernest L. Hall, Editor(s)

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