Diagonal principal component analysis for face recognition

被引：82
|
作者：
Zhang, DQ
Zhou, ZH [1 ]
Chen, SC
机构：
[1] Nanjing Univ, Natl Lab Novel Software Technol, Nanjing 210093, Peoples R China
[2] Nanjing Univ Aeronaut & Astronaut, Dept Comp Sci & Engn, Nanjing 210016, Peoples R China
来源：
PATTERN RECOGNITION | 2006年 / 39卷 / 01期
基金：
中国国家自然科学基金;
关键词：
principal component analysis (PCA); diagonal PCA; 2-dimensional PCA; face recognition;
D O I：
10.1016/j.patcog.2005.08.002
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 paper, a novel subspace method called diagonal principal component analysis (DiaPCA) is proposed for face recognition. In contrast to standard PCA, DiaPCA directly seeks the optimal projective vectors from diagonal face images without image-to-vector transformation. While in contrast to 2DPCA, DiaPCA reserves the correlations between variations of rows and those of columns of images. Experiments show that DiaPCA is much more accurate than both PCA and 2DPCA. Furthermore, it is shown that the accuracy can be further improved by combining DiaPCA with 2DPCA. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：140 / 142
页数：3
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