Face recognition using kernel entropy component analysis

被引：37

作者：

Shekar, B. H. ^{[2
]}

Kumari, M. Sharmila ^{[1
]}

Mestetskiy, Leonid M. ^{[3
]}

Dyshkant, Natalia F. ^{[3
]}

机构：

[1] PA Coll Engn, Dept Comp Sci & Engn, Mangalore, Karnataka, India

[2] Mangalore Univ, Dept Comp Sci, Mangalore, Karnataka, India

[3] Moscow MV Lomonosov State Univ, Dept Computat Math & Cybernet, Moscow, Russia

来源：

NEUROCOMPUTING | 2011年 / 74卷 / 06期

关键词：

Principal component analysis; Entropy component analysis; Eigenface; Face recognition; EIGENFACES;

D O I：

10.1016/j.neucom.2010.10.012

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this letter, we have reported a new face recognition algorithm based on Renyi entropy component analysis. In the proposed model, kernel-based methodology is integrated with entropy analysis to choose the best principal component vectors that are subsequently used for pattern projection to a lower-dimensional space. Extensive experimentation on Yale and UMIST face database has been conducted to reveal the performance of the entropy based principal component analysis method and comparative analysis is made with the kernel principal component analysis method to signify the importance of selection of principal component vectors based on entropy information rather based only on magnitude of eigenvalues. (C) 2010 Elsevier B.V. All rights reserved.

引用

页码：1053 / 1057

页数：5

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