2D Principal Component Analysis for Face and Facial-Expression Recognition

被引：28

作者：

Oliveira, Luiz S. ^{[1
]}

Koerich, Alessandro L. ^{[3
]}

Mansano, Marcelo

Britto, Alceu S., Jr. ^{[2
]}

机构：

[1] Univ Fed Parana, Dept Informat, Grad Program Comp Sci, BR-80060000 Curitiba, Parana, Brazil

[2] Pontificia Univ Catolica Parana, Postgrad Program Appl Informat, Curitiba, Parana, Brazil

[3] Univ Fed Parana, Dept Elect Engn, BR-80060000 Curitiba, Parana, Brazil

来源：

COMPUTING IN SCIENCE & ENGINEERING | 2011年 / 13卷 / 03期

关键词：

2-DIMENSIONAL PCA; REPRESENTATION; METHODOLOGY; ALGORITHMS;

D O I：

10.1109/MCSE.2010.149

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Although it shows enormous potential as a feature extractor, 2D principal component analysis produces numerous coefficients. Using a feature-selection algorithm based on a multiobjective genetic algorithm to analyze and discard irrelevant coefficients offers a solution that considerably reduces the number of coefficients, while also improving recognition rates.

引用

页码：9 / 13

页数：5

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