Research on the selection of kernel function in SVM based facial expression recognition

被引：0
|
作者：
Wang, Fuguang [1 ]
He, Ketai [1 ]
Liu, Ying [1 ]
Li, Li [2 ]
Hu, Xiaoguang [2 ]
机构：
[1] Univ Sci & Technol Beijing, Sch Mech Engn, Beijing 100083, Peoples R China
[2] BeiHang Univ, Sch Automat Sci Elect Engn, Beijing, Peoples R China
来源：
PROCEEDINGS OF THE 2013 IEEE 8TH CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA) | 2013年
关键词：
Support vector machine; polynomial kernel function; RBF kernal function; Facial expression recognition; SUPPORT VECTOR MACHINES;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Support vector machine(SVM) means that structural risk minimization principle is used to substitute Empirical risk minimization principle. SVM has shown the excellent performance in pattern recognition. The kernel function is the core of SVM, with which SVM can help to resolve many kinds of non-linear classification problems. Different kernel models and parameters have different result in the performance of the facial expression recognition system. The authors analyze the capability of polynomial kernel function and RBF kernel function in the facial expression recognition using the JAFFE expressions library. The work is valuable in the choise of kernel and its parameters in practice.
引用
收藏
页码：1404 / 1408
页数：5
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