Face Recognition Using Kernel Methods
核方法在人脸识别中的应用

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.