The Symmetrical Variation of 2DPCA for Face Recognition

被引：0

作者：

Yue, Zeng ^{[1
,2
]}

Feng Dazheng ^{[2
]}

Li, Xiong ^{[2
]}

机构：

[1] Jiangxi Vocat Coll Finance & Econ, Informat Dept Engineer, Jiujiang 332000, Peoples R China

[2] Xidian Univ, Xian, Peoples R China

来源：

ADVANCES IN CIVIL ENGINEERING, PTS 1-6 | 2011年 / 255-260卷

关键词：

PCA; 2DPCA; Face recognition; image representation; REPRESENTATION;

D O I：

10.4028/www.scientific.net/AMR.255-260.2004

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

This paper first discusses the relationship of Principal Component Analysis (PCA) and two-dimensional PCA (2DPCA). For 2DPCA eliminating the some covariance information which can be useful for recognition, The symmetrical Variation of 2DPCA for Face recognition (V2DPCA) is proposed. These experiments on both of ORL face bases shows improvement in recognition accuracy, fewer coefficients and recognition time over 2DPCA, and this algorithm is also superior to the traditional eigenfaces, ICA and Kernel eigenfaces in terms of the recognition accuracy.

引用

页码：2004 / +

页数：2

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