FACIAL EXPRESSION RECOGNITION WITH ROBUST COVARIANCE ESTIMATION AND SUPPORT VECTOR MACHINES

被引：0
|
作者：
Vretos, N. [1 ]
Tefas, A. [1 ]
Pitas, I. [1 ]
机构：
[1] Univ Thessaloniki, Dept Informat, Thessaloniki 54124, Greece
来源：
2012 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP) | 2012年
关键词：
D O I：
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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, a new framework for facial expression recognition is presented. A Support Vector Machine (SVM) variant is proposed, which makes use of robust statistics. We investigate the use of statistically robust location and dispersion estimators, in order to enhance the performance of a facial expression recognition algorithm by using the support vector machines. The efficiency of the proposed method is tested for two-class and multi-class classification problems. In addition to the experiments conducted in facial expression database we also conducted experiments on classification databases to provide evidence that our method outperforms state of the art methods.
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页数：5
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