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Face Recognition under Varying Illumination

Face Recognition by a robot or machine is one of the challenging research topics in the recent years. It has become an active research area which crosscuts several disciplines such as image processing, pattern recognition, computer vision, neural networks and robotics. For many applications, the performances of face recognition systems in controlled environments have achieved a satisfactory level. However, there are still some challenging issues to address in face recognition under uncontrolled conditions. The variation in illumination is one of the main challenging problems that a practical face recognition system needs to deal with. It has been proven that in face recognition, differences caused by illumination variations are more significant than differences between individuals (Adini et al., 1997). Various methods have been proposed to solve the problem. These methods can be classified into three categories, named face and illumination modeling, illumination invariant feature extraction and preprocessing and normalization. In this chapter, an extensive and state-of-the-art study of existing approaches to handle illumination variations is presented. Several latest and representative approaches of each category are presented in detail, as well as the comparisons between them. Moreover, to deal with complex environment where illumination variations are coupled with other problems such as pose and expression variations, a good feature representation of human face should not only be illumination invariant, but also robust enough against pose and expression variations. Local binary pattern (LBP) is such a local texture descriptor. In this chapter, a detailed study of the LBP and its several important extensions is carried out, as well as its various combinations with other techniques to handle illumination invariant face recognition under a complex environment. By generalizing different strategies in handling illumination variations and evaluating their performances, several promising directions for future research have been suggested. This chapter is organized as follows. Several famous methods of face and illumination modeling are introduced in Section 2. In Section 3, latest and representative approaches of illumination invariant feature extraction are presented in detail. More attentions are paid on quotient-image-based methods. In Section 4, the normalization methods on discarding low frequency coefficients in various transformed domains are introduced with details. In Section 5, a detailed introduction of the LBP and its several important extensions is presented, as well as its various combinations with other face recognition techniques. In Section 6, comparisons between different methods and discussion of their advantages and disadvantages are presented. Finally, several promising directions as the conclusions are drawn in Section 7.