Photometric Normalization Techniques for Illumination Invariance

Face recognition technology has come a long way since its beginnings in the previous century. Due to its countless application possibilities in both the private as well as the public sector, it has attracted the interest of research groups from universities and companies around the world. Thanks to this enormous research effort, the recognition rates achievable with the state-of-the-art face recognition technology are steadily growing, even though some issues still pose major challenges to the technology. Amongst these challenges, coping with illumination induced appearance variations is one of the biggest and still not satisfactorily solved. A number of techniques have been proposed in the literature to cope with the illumination induced appearance variations ranging from simple image enhancement techniques, such as histogram equalization or gamma intensity correction, to more elaborate methods, such as homomorphic filtering, anisotropic smoothing or the logarithmic total variation model. This chapter presents an overview of the most popular and efficient normalization techniques which try to solve the illumination variation problem at the preprocessing level. It assesses the techniques on the publicly available YaleB face database and explores their strengths and weaknesses from the theoretical and implementational point of view.