Quality-Driven Face Occlusion Detection and Recovery

This paper presents a framework to automatically detect and recover the occluded facial region. We first derive a Bayesian formulation unifying the occlusion detection and recovery stages. Then a quality assessment model is developed to drive both the detection and recovery processes, which captures the face priors in both global correlation and local patterns. Based on this formulation, we further propose GraphCut-based detection and confidence-oriented sampling to attain optimal detection and recovery respectively. Compared to traditional works in image repairing, our approach is distinct in three aspects: (1) it frees the user from marking the occlusion area by incorporating an automatic occlusion detector; (2) it learns a face quality model as a criterion to guide the whole procedure; (3) it couples the detection and occlusion stages to simultaneously achieve two goals: accurate occlusion detection and high quality recovery. The comparative experiments show that our method can recover the occluded faces with both the global coherence and local details well preserved.