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Optical Engineering

Locality projection discriminant analysis with an application to face recognition
Author(s): Xuchu Wang; Yanmin Niu
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Paper Abstract

A locality projection discriminant analysis (LPDA) method by using local structure-based projection techniques is proposed to extract discriminative features in high-dimensional sample space. Two locality metric matrices are defined to make the discriminative projections preserve the intrinsic neighborhood geometry of the within-class samples while enlarging the margins of extra-class samples near to the class boundaries. LPDA efficiently addresses the nonlinear property of data and the small sample size problem in face recognition scenario; moreover, it can reduce the dimensionality of the original data (such as the role of principle component analysis) as well as extract complete discriminative features in dual subspaces. Experiments on synthetic data sets and ORL, PIE, and FERET low-resolution face databases are performed to evaluate LPDA-based methods and some known methods. The results demonstrate the effectiveness of LPDA and reveal some characteristics of this pure local structure-based method.

Paper Details

Date Published: 1 July 2010
PDF: 12 pages
Opt. Eng. 49(7) 077201 doi: 10.1117/1.3463017
Published in: Optical Engineering Volume 49, Issue 7
Show Author Affiliations
Xuchu Wang, Chongqing Univ. (China)
Yanmin Niu, Chongqing Univ. (China)

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