%0 Journal Article
%T Face Recognition Using Kernel Methods<br>核方法在人脸识别中的应用
%A ZHU Mei-Lin LIU Xiang-Dong CHEN Shi-Fu
%A <br>朱美琳
%A 刘向东
%A 陈世福
%J 计算机科学
%D 2003
%I 
%X Kernel function is the function which computes dot product in feature spaces. Both the SVMs and kernel PCA are kernel.based learning methods. In this paper, the SVMs and kernel PCA are used to tackle the face recognition problem. SVMs are classifiers which have demonstrated high generalization capabilities. Kernel PCA is a feature extraction technique which is proposed as a nonlinear extension of a PCA. We illustrate the potential of SVMs and kernel PCA on the Yale database and compare with a PCA based algorithm. The experiments indicate that SVMs and kernel PCA are superior to the PCA method.
%K Kernel methods
%K Face recognition
%K Support vector machines
%K Kernel principal component analysis
%K Principalcomponent analysis<br>人脸识别
%K 核方法
%K 模式识别
%K 人脸图像
%K 几何特征
%K 模板匹配
%K 图像识别
%K 图像处理
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=D772E0787C6DF45D&yid=D43C4A19B2EE3C0A&vid=340AC2BF8E7AB4FD&iid=94C357A881DFC066&sid=0D0D661F0B316AD5&eid=656F8C8401D91023&journal_id=1002-137X&journal_name=计算机科学&referenced_num=1&reference_num=23