Comparison of principal component analysis and linear discriminant analysis for face recognition (March 2007)

Abstract

In this paper two face recognition techniques, principal component analysis (PCA) and linear discriminant analysis (LDA), are considered and implemented using a nearest neighbor classifier. The performance of the two techniques is then compared in facial recognition and detection tasks. The comparisons are done using a facial recognition database captured for the project that contains images captured over a range of poses, lighting conditions and occlusions.