核方法在人脸识别中的应用

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.