Fusion of lighting insensitive approaches for illumination robust face recognition

In this paper the problem of finding a face recognition system that works well both under variable illumination conditions and under strictly controlled acquisition conditions is considered. The problem under consideration has to do with the fact that systems that work well (compared with standard methods) with variable illumination conditions often suffer a drop in performance on images where illumination is strictly controlled. In this chapter we review existing techniques for obtaining illumination robustness and propose a method for handling illumination variance that combines different matchers and preprocessing methods. An extensive evaluation of our system is performed on several datasets (CMU, ORL, Extended YALE-B, and BioLab). Our results show that even though some standalone matchers are inconsistent in performance depending on the database, the fusion of different methods performs consistently well across all tested datasets and illumination conditions. Our experiments show that the best result are obtained using gradientfaces as a preprocessing method and orthogonal linear graph embedding as a feature transform.