Principal Component Analysis in Linear Discriminant Analysis Space for Face Recognition

Principal component analysis (PCA) is an effective statistical technique for face recognition because it can reduce the dimensions of a given unlabeled high-dimensional dataset while keeping its spatial characteristics as much as possible. However, since PCA only explains the covariance structure of all the data its most expressive components, it cannot represent the most important discriminant directions to separate sample groups. To solve this problem, in this paper we propose a new PCA method based on the linear discriminant analysis (LDA) space. From our theoretic analysis and numerical experiments, our new PCA method (we call it PCA-LDA) can work effectively and efficiently.