Structured occlusion coding for robust face recognition

NEUROCOMPUTING

卷:178

Article

Wen, Yandong¹  Liu, Weiyang²  Yang, Meng³  Fu, Yuli¹  Xiang, Youjun¹  Hu, Rui¹ 

[1] S China Univ Technol, Sch Elect & Informat Engn, Guangzhou 510641, Guangdong, Peoples R China

[2] Peking Univ, Sch Elect & Comp Engn, Beijing, Peoples R China

[3] Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen, Peoples R China

关键词: Sparse representation based classification;    Occlusion dictionary;    Occlusion mask estimating;    Locality constrained dictionary;    Structured sparsity;   

DOI  :  10.1016/j.neucom.2015.05.132

来源: Elsevier

【 摘 要 】

Occlusion in face recognition is a common yet challenging problem. While sparse representation based classification (SRC) has been shown promising performance in laboratory conditions (i.e. noiseless or random pixel corrupted), it performs much worse in practical scenarios. In this paper, we consider the practical face recognition problem, where the occlusions are predictable and available for sampling. We propose the structured occlusion coding (SOC) to address occlusion problems. The structured coding here lies in two folds. On one hand, we employ a structured dictionary for recognition. On the other hand, we propose to use the structured sparsity in this formulation. Specifically, SOC simultaneously separates the occlusion and classifies the image. In this way, the problem of recognizing an occluded image is turned into seeking a structured sparse solution on occlusion-appended dictionary. In order to construct a well performing occlusion dictionary, we propose an occlusion mask estimating technique via locality constrained dictionary (LCD), showing striking improvement in occlusion sample. On a category-specific occlusion dictionary, we replace l(1) norm sparsity with the structured sparsity which is shown more robust, further enhancing the robustness of our approach. Moreover, SOC achieves significant improvement in handling large occlusion in real world. Extensive experiments are conducted on public data sets to validate the superiority of the proposed algorithm. (C) 2015 Elsevier B.V. All rights reserved.

【 授权许可】

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