Abstract：In the occlusion face recognition, some covered parts change the property of local information. It may lead to a wrong classification using the minimum residual as a decision function for sparse representation classification when the residual is approximate. In this case, proceeding from the decision rule of the classifier, the algorithm of sparse representation with weighted fusion of local based non-minimum square error and global is proposed for face recognition. The accumulation of each class of coefficient is mainly used as the decision function and the Borda votes system is introduced for sparse representation classification. Firstly, the sparse coefficient accumulation of each class is calculated for global classification. Then, for the local information, the subblocks coefficient accumulation is used to classify. Considering the different effects of subblocks, the sparsity and residual are utilized to jointly express the weight of credibility. Finally, the global and local blocks are combined to Borda vote for the final classification. The experimental results on public available database demonstrate that the proposed algorithm has good effectiveness and robustness.
胡正平，彭燕，赵淑欢. 非最小平方误差局部－全局加权融合的稀疏表示遮挡人脸识别*[J]. 模式识别与人工智能, 2015, 28(7): 633-640.
HU Zheng-Ping, PENG Yan, ZHAO Shu-Huan. Sparse Representation with Weighted Fusion of Local Based Non-minimum Square Error and Global for Face Recognition under Occlusion Condition. , 2015, 28(7): 633-640.
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2015, Vol. 28 
