PCA plus F-LDA: A new approach to face recognition

被引：4

作者：

Wang, Huiyuan ^{[1
]}

Wang, Zengfeng ^{[2
]}

Leng, Yan ^{[3
]}

Wu, Xiaojuan ^{[4
]}

Li, Qing ^{[5
]}

机构：

[1] Shandong Univ, Sch Informat Sci & Engn, Jinan 250100, Shandong, Peoples R China

[2] Ludong Univ, Sch Comp Sci & Technol, Yantai 264025, Shandong, Peoples R China

[3] Shandong Normal Univ, Coll Phys & Elect, Jinan 250014, Shandong, Peoples R China

[4] Shandong Univ, Sch Informat Sci & Engn, Jinan 250100, Shandong, Peoples R China

[5] Shandong Meteorol Bur, Jinan 250031, Shandong, Peoples R China

来源：

INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE | 2007年 / 21卷 / 06期

基金：

中国国家自然科学基金;

关键词：

face recognition; feature extraction; subspace analysis; linear discriminant analysis; fractional-step linear discriminant analysis;

D O I：

10.1142/S021800140700579X

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A new feature extraction method for face recognition based on principal component analysis (PCA) and fractional-step linear discriminant analysis (F-LDA) is given in this paper. In order to reduce the computation complexity, PCA is first used to reduce the dimension. In addition, before using F-LDA, we transform the pooled within-class scatter matrix into an identity matrix. The proposed method is tested on AR and UMIST face databases. Experiment results show that our method gains higher classification accuracy than other existing methods used in the experiment.

引用

页码：1059 / 1068

页数：10

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