INPAINTING OF SPARSE OCCLUSION IN FACE RECOGNITION

Facial occlusion is a critical issue in many face recognition applications. Existing approaches of face recognition under occlusion conditions mainly focus on the conventional facial accessories (such as sunglasses and scarf) and thus presume that the occluded region is dense and contiguous. Yet due to the wide variety of natural sources which can occlude a human face in uncontrolled environments, methods based on the dense assumption are not robust to thin and randomly distributed occlusions. This paper presents the solution to a newly identified facial occlusion problem - sparse occlusion in the context of face biometrics in video surveillance. We show that the occluded pixels can be detected in the low-rank structure of a canonical face set under the Robust-PCA framework; and the occluded part can be inpainted solely based on the non-occluded part and a Fields-of-Experts prior via spatial inference. Experiments demonstrate that the proposed approach significantly improve various face recognition algorithms in presence of complex sparse occlusions.