Face recognition using the mixture-of-eigenfaces method

Abstract

This paper deals with face recognition using the mixture-of-eigenfaces method. The well-known eigenface method uses one set of holistic facial features obtained by principal component analysis (PCA). However, a single set of eigenfaces is not enough to represent face images with large variations. To overcome this weakness, we propose the mixture-of-eigenfaces method, which uses more than one set of eigenfaces obtained from the expection maximization learning in the PCA mixture model. In this method, several sets of eigenfaces are obtained from all face images, and each template face image is represented by an appropriate set of eigenfaces. Recognition was performed using the distance between the input image and the labelled template image stored in the face database, where the distance is the difference of the feature values that are obtained from the set of eigenfaces indicated by the labelled template image. Simulation results show that the proposed mixture-of-eigenfaces method outperforms the eigenface method in terms of recognition accuracy for face images with pose and illumination variations. (C) 2002 Elsevier Science B.V. All rights reserved.