SEA  >> Vol. 5 No. 5 (October 2016)

    Face Recognition Research Based on Sparse Representation of Blocks

  • 全文下载: PDF(701KB) HTML   XML   PP.277-284   DOI: 10.12677/SEA.2016.55032  
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路 杨,康瑞梅,张芳君:河南大学计算机与信息工程学院,河南 开封

稀疏表示人脸识别分块遮挡Sparse Representation Face Recognition Block Occlusion



In order to reduce the sensitivity of the face recognition algorithm to occlusion, a robust occlusion block sparse representation classification face recognition algorithm is proposed. The sparse representation algorithm uses the sparsity of high-dimensional data distribution to perform modeling, which can deal with high-dimensional image and effectively avoid dimension disaster. Block thinking is introduced in this algorithm. First of all, face image is divided into blocks which are independently sparse representation classification, and then a joint determination by all classification sub-blocks. The improved algorithm not only avoids the image feature extraction process information loss caused, but also avoids the loss of face parts information on the overall recognition results. Through simulation experiments on AR and Yale face database, it can be drawn that the improved algorithm can significantly improve the recognition rate of occluded face image, and also have some certain robustness under variable illumination.

路杨, 康瑞梅, 张芳君. 分块稀疏表示的人脸识别研究[J]. 软件工程与应用, 2016, 5(5): 277-284.


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