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Face recognition with occlusion is common in the real world. Inspired by the works of structured sparse representation, we try to explore the structure of the error incurred by occlusion from two aspects: the error morphology and the error distribution. Since human beings recognize the occlusion mainly according to its region shape or profile without knowing accurately what the occlusion is, we argue...
Partially occluded faces are common in automatic face recognition in the real world. Existing methods, such as sparse error correction with Markov random fields, correntropy-based sparse representation and robust sparse coding, are all based on error correction, which relies on the perfect reconstruction of the occluded facial image and limits their recognition rates especially when the occluded regions...
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