Independent Component Analysis of Gabor Features for Facial Expression Recognition

被引：4
|
作者：
Zhang Qiang [1 ]
Chen Chen [1 ]
Zhou Changjun [1 ]
Wei Xiaopeng [1 ]
机构：
[1] Dalian Univ, Liaoning Key Lab Intelligent Informat Proc, Dalian 116622, Peoples R China
来源：
ISISE 2008: INTERNATIONAL SYMPOSIUM ON INFORMATION SCIENCE AND ENGINEERING, VOL 1 | 2008年
关键词：
facial expression recognition; Gabor feature; ICA; independent Gabor features; SVM;
D O I：
10.1109/ISISE.2008.323
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper proposes a novel method for facial expression recognition by using independent component analysis of Gabor features. In the feature extraction stage, Gabor feature vectors are firstly extracted from a set of facial expressions images, then using independent component analysis (ICA) to extract the independent Gabor features. After that, the independent Gabor features are used to train SVM to realize the facial expression recognition, and the computer simulation illustrates the effectivity of this method to classify the seven expressions of the JAFFE database.
引用
收藏
页码：84 / 87
页数：4
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