Discriminant Analysis of Principal Components for Face Recognition

In this paper we describe a face recognition method based on PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). The method consists of two steps: rst we project the face image from the original vector space to a face subspace via PCA, second we use LDA to obtain a best linear clas-siier. The basic idea of combining PCA and LDA is to improve the generalization capability of LDA when only few samples per class are available. Using PCA, we are able to construct a face subspace in which we apply LDA to perform classiication. Using FERET dataset we demonstrate a signiicant improvement when principal components rather than original images are fed to the LDA classiier. The hybrid clas-siier using PCA and LDA provides a useful framework for other image recognition tasks as well.