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.
引用
收藏
页码：1048 / +
页数：3
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