Dense Wide-Baseline Stereo with Varying Illumination and its Application to Face Recognition

We study the problem of dense wide baseline stereo with varying illumination. We are motivated by the problem of face recognition across pose. Stereo matching allows us to compare face images based on physically valid, dense correspondences. We show that the stereo matching cost provides a very robust measure of the similarity of faces that is insensitive to pose variations. We build on the observation that most illumination insensitive local comparisons require the use of relatively large windows. The size of these windows is affected by foreshortening. If we do not account for this effect, we incur misalignments that are systematic and significant and are exacerbated by wide baseline conditions. We present a general formulation of dense wide baseline stereo with varying illumination and provide two methods to solve them. The first method is based on dynamic programming (DP) and fully accounts for the effect of slant. The second method is based on graph cuts (GC) and fully accounts for the effect of both slant and tilt. The GC method finds a global solution using the unary function from the general formulation and a novel smoothness term that encodes surface orientation. Our experiments show that DP dense wide baseline stereo achieves superior performance compared to existing methods in face recognition across pose. The experiments with the GC method show that accounting for both slant and tilt can improve performance in situations with wide baselines and lighting variation. Our formulation can be applied to other more sophisticated window based image comparison methods for stereo