Complete Pose Binary SIFT for Face Recognition with Pose Variation

摘要

Some pose invariant face recognition approaches require preprocessing such as face alignment or landmark fitting, which is another unresolved problem. SIFT based face recognition schemes could resolve the problem of constrained pose variation without such preprocessing, we find that the sift descriptors are robust to off-plane rotation within 25 degree and in-plane rotation. Furthermore, we propose complete pose binary SIFT (CPBS) to address the issue of arbitrary pose variation. First, five face images with poses of frontal view, rotation left/right 45 and 90 degree respectively are selected as gallery images of a subject. Then the binary descriptors of these images are pooled together as CPBS of the subject. Face recognition is finished by hamming distance between the probe face image and the CPBS. Experimental results on the CMU-PIE and FERET face databases show that our approach has performance comparable to state-of-the-art approaches, while not requiring face alignment or landmark fitting.