Kernel modified quadratic discriminant function for facial expression recognition

被引：0
|
作者：
Yang, Duan-Duan [1 ]
Jin, Lian-Wen
Yin, Jun-Xun
Zhen, Li-Xin
Huang, Jian-Cheng
机构：
[1] S China Univ Technol, Dept Elect & Commun Engn, Guangzhou 510640, Peoples R China
[2] Motorola China Res Ctr, Shanghai 210000, Peoples R China
来源：
ADVANCES IN MACHINE VISION, IMAGE PROCESSING, AND PATTERN ANALYSIS | 2006年 / 4153卷
关键词：
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：
The Modified Quadratic Discriminant Function was first proposed by Kimura et al to improve the performance of Quadratic Discriminant Function, which can be seen as a dot-product method by eigen-decompostion of the covariance matrix of each class. Therefore, it is possible to expand MQDF to high dimension space by kernel trick. This paper presents a new kernel-based method to pattern recognition, Kernel Modified Quadratic Discriminant Function (KMQDF), based on MQDF and kernel method. The proposed KMQDF is applied in facial expression recognition. JAFFE face database and the AR face database are used to test this algorithm. Experimental results show that the proposed KMQDF with appropriated parameters can outperform 1-NN, QDF, MQDF classifier.
引用
收藏
页码：66 / 75
页数：10
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