Subspace-based face recognition: outlier detection and a new distance criterion

Abstract

Illumination effects, including shadows and varying lighting, makes the problem of face recognition challenging. Experimental and theoretical results show that the face images under different illumination conditions lie in a low-dimensional subspace, hence principal component analysis (PCA) or low-dimensional subspace techniques have been used. Following this spirit, we propose new techniques for the face recognition problem, including an outlier detection strategy (mainly for those points not following the Lambertian reflectance model), and a new Bayesian-based error criterion for the recognition algorithm. Experiments using the Yale-B face database show the effectiveness of the new strategies