Face Recognition Method Based on Independent Component Analysis and Support Vector Machine

被引：0

作者：

Kong, Rui ^{[1
]}

Zhang, Bing ^{[1
]}

机构：

[1] Jinan Univ, Sch Elect & Informat, Zhuhai, Guangdong, Peoples R China

来源：

2011 INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTATION AND INDUSTRIAL APPLICATION (ICIA2011), VOL I | 2011年

关键词：

Face Recognition; Independent Component Analysis; Principal Component Analysis; Support Vector Machine;

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：

In this paper we propose a new method for face recognition by combining Independent Component Analysis(ICA) and Support Vector Machine(SVM). Firstly we extract face features by using Independent Component Analysis. We then implement face recognition tests. By using ORL face data, We compare classical method of Principal Component Analysis(PCA) and our new method. The experiment results show the performance of our method is significantly superior to that of PCA-based method. The speed of our method is faster than that of classical SVM algorithms.

引用

页码：118 / 121

页数：4

共 7 条

[1] [Anonymous], IEEE T PATTERN ANAL
[2] BARTLETT MS, 1998, THESIS
[3] AN INFORMATION MAXIMIZATION APPROACH TO BLIND SEPARATION AND BLIND DECONVOLUTION
BELL, AJ
SEJNOWSKI, TJ
[J]. NEURAL COMPUTATION, 1995, 7 (06) : 1129 - 1159
[4] Kong Rui, 2007, Computer Engineering and Applications, V43, P168
[5] Scholkopf B., 1998, Advances in Kernel Methods: Support Vector Learning
[6] Least squares support vector machine classifiers
Suykens, JAK
Vandewalle, J
[J]. NEURAL PROCESSING LETTERS, 1999, 9 (03) : 293 - 300
[7] Vapnik V., 1995, The nature of statistical learning theory

← 1 →