A methodology for evaluating robustness of face recognition algorithms with respect to variations in pose angle and illumination angle

In this paper, we present a methodology for precisely comparing the robustness of face recognition algorithms with respect to changes in pose angle and illumination angle. For this study, we have chosen four widely-used algorithms: two subspace analysis methods (principle component analysis (PCA) and linear discriminant analysis (LDA)) and two probabilistic learning methods (hidden Markov models (HMM) and Bayesian intra-personal classifier (BIC)). We compare the recognition robustness of these algorithms using a novel database (FacePix) that captures face images with a wide range of pose angles and illumination angles. We propose a method for deriving a robustness measure for each of these algorithms, with respect to pose and illumination angle changes. The results of this comparison indicate that the subspace methods perform more robustly than the probabilistic learning methods in the presence of pose and illumination angle changes.