Segmented linear subspaces for illumination-robust face recognition

All images of a convex Lambertian surface captured with a fixed pose under varying illumination are known to lie in a convex cone in the image space that is called the illumination cone. Since this cone model is too complex to be built in practice, researchers have attempted to approximate it with simpler models. In this paper, we propose a segmented linear subspace model to approximate the cone. Our idea of segmentation is based on the fact that the success of low dimensional linear subspace approximations of the illumination cone increases if the directions of the surface normals get close to each other. Hence, we propose to cluster the image pixels according to their surface normal directions and to approximate the cone with a linear subspace for each of these clusters separately. We perform statistical performance evaluation experiments to compare our system to other popular systems and demonstrate that the performance increase we obtain is statistically significant.