关键词: 支持向量机,人脸表情识别,高斯核,最小二乘算法 中图法分类号TP391.4文献标识码A

Abstract: Aiming at the map of the low dimension data space for support vector machines (SVM) to high dimension feature space,the Riemann measure tensor was used to extend the SVM linear disjunctive borders,and the SVM classification accuracy was improved．In fact,the good multiple resolution approximation accuracy of MLS-LSM has the similar process to that of the face emotion identification of human eyes．Therefore,one face emotion identification algorithm with the improved MLS-SVM by Guass kernel function was proposed in this paper,which could improve the face emotion identification accurate．At last,the numerical evaluation results show that the accurate of face emotion identification is improved with the comparison to two classification approaches.

Key words: Support vector machine,Face emotion identification,Gauss kernel function,Least square algorithm