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Quality-Driven Face Occlusion Detection and Recovery
| Content Provider | CiteSeerX |
|---|---|
| Author | Lin, Dahua |
| Abstract | 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. 1. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Quality-driven Face Occlusion Detection Propose Graphcut-based Detection Automatic Occlusion Detector Occlusion Stage Face Prior Comparative Experiment Confidence-oriented Sampling Local Pattern Quality Assessment Model Global Coherence Bayesian Formulation Image Repairing Occlusion Area Occlusion Detection Facial Region Optimal Detection Local Detail Whole Procedure Accurate Occlusion Detection Traditional Work Recovery Stage Face Quality Model Global Correlation High Quality Recovery Recovery Process |
| Content Type | Text |
| Resource Type | Article |
