Image factorization and feature fusion for enhancing robot vision in human face recognition

Illumination variation has been a challenging problem for face recognition in robot vision. To reduce the effect caused by illumination variation, a lot of studies have been explored. The Total Variation (TV) method is particular used to factorize images into a low frequency component and a high frequency one. However, the low frequency component still contains significant intrinsic features resulting in failure in face recognition in some cases. In this paper, we propose to further extract illumination invariant features from face images under uncontrolled varying lighting conditions. The Nonsampled Contourlet Transform (NSCT) method is employed to enhance the extraction of intrinsic feature. The combined factorization model is very effective in the experiment on the Yale database.