An experimental evaluation of linear and kernel-based methods for face recognition

Abstract

In this paper we present the results of a comparative study of linear and kernel-based methods for face recognition. The methods used for dimensionality reduction are Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA), Linear Discriminant Analysis (LDA) and Kernel Discriminant Analysis (KDA). The methods used for classification are Nearest Neighbor (NN) and Support Vector Machine (SVM). In addition, these classification methods are applied on raw images to gauge the performance of these dimensionality reduction techniques. All experiments have been performed on images from UMIST Face Database.