Symmetry based two-dimensional principal component analysis for face recognition

被引：0
|
作者：
Ding, Mingyong [1 ]
Lu, Congde [2 ]
Lin, Yunsong [2 ]
Tong, Ling [2 ]
机构：
[1] Chongqing Technol & Business Univ, Sch Comp Sci & Informat Engn, Chongqing 400067, Peoples R China
[2] Univ Elect Sci & Technol China, Sch Automat Engn, Chengdu 610054, Peoples R China
来源：
ADVANCES IN NEURAL NETWORKS - ISNN 2007, PT 2, PROCEEDINGS | 2007年 / 4492卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Two-dimensional principal component analysis (2DPCA) proposed recently overcome a limitation of principal component analysis (PCA) which is expensive computational cost. Symmetrical principal component analysis (SPCA) is also a better feature extraction technique because it utilizes effectively the symmetrical property of human face. This paper presents a symmetry based two-dimensional principal component analysis (S2DPCA), which combines the advantages of 2DPCA and of the SPCA. The experimental results show that S2DPCA is competitive with or superior to 2DPCA and SPCA.
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页码：1048 / +
页数：3
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