Face alignment using intrinsic information

Previous 2-D face alignment algorithms are generally quite sensitive to illumination variation and poor initialization. To account for these two obstacles, two forms of relatively lighting invariant descriptors - intrinsic gray-level information and intrinsic edge information - rare adopted in our algorithm to direct shape search. The former is recovered from local intensity normalization and useful at localizing face contours accurately despite its dependency on initialization. The latter is extracted from normalized local regions by Canny edge filtering and is robust at coarse alignment in spite of poor initialization. The different merits of these two forms of intrinsic information motivate us to employ them at different stages of our face alignment process. Extensive experimentations show that this proposed approach allows our system to handle not only illumination variation, but also poor initialization.