支持向量机在人脸识别中的应用

对于人脸识别问题,基于K-L变换对人脸图像进行特征参数的提取,并采用支持向量机进行分类。由于支持向量机本身是一个两类问题的判别方法,在处理多类问题时,提出了一种基于支持向量机组的淘汰法,这种方法考虑到了各判别函数的VC置信范围的差异,同时利用判别函数间的冗余来降低识别误差。在对ORL人脸库和自建的人脸库的测试中,分别得到识别率为97.5%和90.59%的实验结果,这些结果表明,基于SVM的识别方法是有效的。

Face Recognition Based on Support Vector Machines

In face recognition,Karhunen-Loeve transform is employed to get the representation basis of face image set,and support vector machines are used to classify.Support Vector Machines(SVM)are classifiers which have demonstrated high generalization capabilities.SVM group incorporated with the elimination strategy is proposed to deal with the multi-class face recognition problem.For considering the differences among the confidence interval about the SVM and the redundancy of each SVM,a higher recognition rate can be reached.The face recognition experiment with the two face databases shows that the proposed method has reached a higher recognition rate and a reasonable time cost.