Nearest-subspace patch matching for face recognition under varying pose and illumination

We consider the problem of recognizing human faces despite variations in both pose and illumination, using only frontal training images. We propose a very simple algorithm, called Nearest-Subspace Patch Matching, which combines a local translational model for deformation due to pose with a linear subspace model for lighting variations. This algorithm gives surprisingly competitive performance for moderate variations in both pose and illumination, a domain that encompasses most face recognition applications, such as access control. The results also provide a baseline for justifying the use of more complicated face models or more advanced learning methods to handle more extreme situations. Extensive experiments on publicly available databases verify the efficacy of the proposed method and clarify its operating range.