摘要
Kernel function is the function which computes dot product in feature spaces. Both the SVMs and kernelPCA are kernel-based learning methods. In this paper, the SVMs and kernel PCA are used to tackle the face recogni-tion problem. SVMs are classifiers which have demonstrated high generalization capabilities. Kernel PCA is a featureextraction technique which is proposed as a nonlinear extension of a PCA. We illustrate the potential of SVMs andkernel PCA on the Yale database and compare with a PCA based algorithm. The experiments indicate that SVMs andkernel PCA are superior to the PCA method.
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.
出处
《计算机科学》
CSCD
北大核心
2003年第5期82-84,共3页
Computer Science
关键词
人脸识别
核方法
模式识别
人脸图像
几何特征
模板匹配
图像识别
图像处理
Kernel methods,Face recognition,Support vector machines,Kernel principal component analysis,Principalcomponent analysis
