A two-stage linear discriminant analysis for face-recognition

被引：26
|
作者：
Sharma, Alok [1 ,2 ,3 ]
Paliwal, Kuldip K. [2 ]
机构：
[1] Univ Tokyo, Inst Med Sci, Ctr Human Genome, Lab DNA Informat Anal, Tokyo 1138654, Japan
[2] Griffith Univ, Signal Proc Lab, Nathan, Qld 4111, Australia
[3] Univ S Pacific, Sch Engn & Phys, Suva, Fiji
来源：
PATTERN RECOGNITION LETTERS | 2012年 / 33卷 / 09期
关键词：
Two-stage linear discriminant analysis; Small sample size problem; Classification accuracy; SMALL SAMPLE-SIZE; DIRECT LDA; REGULARIZATION; EIGENFEATURES; MATRIX;
D O I：
10.1016/j.patrec.2012.02.001
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A two-stage linear discriminant analysis technique is proposed that utilizes both the null space and range space information of scatter matrices. The technique regularizes both the between-class scatter and within-class scatter matrices to extract the discriminant information. The regularization is conducted in parallel to give two orientation matrices. These orientation matrices are concatenated to form the final orientation matrix. The proposed technique is shown to provide better classification performance on face recognition datasets than the other techniques. (C) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码：1157 / 1162
页数：6
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