Essence of 2DPCA and modification method for face recognition

被引：0
|
作者：
Hou, Jun [1 ]
Gao, Quan-Xue [2 ]
Pan, Quan [1 ]
Zhang, Hong-Cai [1 ]
机构：
[1] Northwestern Polytech Univ, Coll Automat, Xian 710072, Peoples R China
[2] Xidian Univ, Coll Telecommun Engn, Xian 710071, Peoples R China
来源：
PROCEEDINGS OF 2006 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2006年
关键词：
principal component analysis (PCA); 2DPCA; face recognition;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, the method of 2DPCA is analyzed and its nature is revealed, i.e., 2DPCA is equivalent to view rows of face images as training samples that constitute row training sets and then use PCA for feature extraction. We also have proved that principal component vectors extracted by 2DPCA contain redundancy in theory. Based on this result, this paper presents a new image feature extraction method. The proposed method provides a sequentially optimal image compression mechanism. Finally, the effectiveness of the proposed algorithm is verified using the ORL database.
引用
收藏
页码：3351 / +
页数：2
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