Face Recognition with Weighted Kernel Principal Component Analysis

Abstract

Principal component analysis (PCA) is one of the most traditional linear dimensionality reduction algorithms. Kernel principal component analysis (kernel PCA), generalization of PCA, is a nonlinear feature extraction method. However, both PCA and kernel PCA are lack of class information in their feature subspace. In this paper, weighted kernel principal component analysis (WKPCA) is proposed for feature extraction with the application of face recognition. Weights that represent inter-class relationships are incorporated into kernel matrix. Images in training and testing set are projected onto the subspace obtained from weighted kernel matrix. The feature extraction procedure is in a framework of genetic algorithms (GAs) with the fitness as classification accuracy on cross-validation data from training set. The experimental results of WKPCA are compared with PCA and kernel PCA on a combo database (ORL, Yale, UMIST databases), and show that proposed WKPCA algorithm performs best in face recognition