Abstract：Considering the complementation of global and local components, bi-L1 sparse representation algorithm for face recognition based on fusion of global and separated components is proposed. Firstly, based on L1 sparse representation, the global information is used to obtain the global sparse approximation. Then, several slightly overlapping face components are extracted and aligned in the recognition model of separated components. After that, the sparse representation of all the components is obtained respectively. The sparse approximation results of each component are combined with a similarity voting method based on the residuals of class representation. Finally, the weighted integration of the global and components sparse representation is used to construct the bi-L1 sparse representation classifier in decision-making layer. The experimental results on public available database demonstrate that the performance of the integration classifier is superior to that of each single module. Due to the fusion of component information which is insensitive to variation of illumination and expression etc., the robustness of the system is enhanced.
胡正平，宋淑芬. 基于全局和分离部件融合的双L1稀疏表示人脸图像识别算法[J]. 模式识别与人工智能, 2012, 25(2): 256-261.
HU Zheng-Ping, SONG Shu-Fen. Bi-L1 Sparse Representation Algorithm for Face Recognition Based on Fusion of Global and Separated Components. , 2012, 25(2): 256-261.
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2012, Vol. 25 
