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| Sparse Representation with Weighted Fusion of Local Based Non-minimum Square Error and Global for Face Recognition under Occlusion Condition |
| HU Zheng-Ping, PENG Yan, ZHAO Shu-Huan |
| (School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004 |
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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.
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Received: 09 April 2014
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[1] Wright J, Yang A Y, Ganesh A, et al. Robust Face Recognition via Sparse Representation. IEEE Trans on Pattern Analysis and Machine Intelligence, 2009, 31(2): 210-227
[2] Li X X, Dai D Q, Zhang X F, et al. Structured Sparse Error Coding for Face Recognition with Occlusion. IEEE Trans on Image Proce-ssing, 2013, 22(5): 1889-1900
[3] Xu Y, Zhu Q, Chen Y, et al. An Improvement to the Nearest Neighbor Classifier and Face Recognition Experiments. International Journal of Innovative Computing, Information and Control, 2013, 9(2): 543-554
[4] Gao S H, Tsang I W H, Chia L T. Sparse Representation with Kernels. IEEE Trans on Image Processing, 2013, 22(2): 423-434
[5] Gao S H, Tsang I W H, Chia L T. Laplacian Sparse Coding, Hypergraph Laplacian Sparse Coding, and Applications. IEEE Trans on Pattern Analysis and Machine Intelligence, 2013, 35(1): 92-104
[6] Peng Y G, Genesh A, Wright J, et al. RASL: Robust Alignment by Sparse and Low-Rank Decomposition for Linearly Correlated Images. IEEE Trans on Pattern Analysis and Machine Intelligence, 2012, 34(11): 2233-2246
[7] Dornaika F, Bosaghzadeh A. Exponential Local Discriminant Embedding and Its Application to Face Recognition. IEEE Trans on Cybernetics, 2013, 43(3): 921-934
[8] Duan C H, Chiang C K, Lai S H. Face Verification with Local Sparse Representation. IEEE Signal Processing Letters, 2013, 20(2): 177-180
[9] Nikan S, Ahmadi M. Human Face Recognition under Occlusion Using LBP and Entropy Weighted Voting // Proc of the 21st International Conference on Pattern Recognition. Tsukuba, Japan, 2012: 1699-1702
[10] Hu Z P, Li J, Zhao S H. Sub-modular Sparse Representation Algorithm for Robust Pattern Recognition Based on Borda Voted Weighting. Chinese Journal of Scientific Instrument, 2013, 34(10): 2309-2315 (in Chinese)
(胡正平,李 静,赵淑欢.基于Borda投票加权的子模块稀疏表示鲁棒模式识别算法.仪器仪表学报, 2013, 34(10): 2309-2315)
[11] Deng N, Xu Z G, Wang J. Sparse Representation Based Multi-classifier Fusion Approach for Occluded Face Recognition. Application Research of Computers, 2013, 30(6): 1914-1916, 1920 (in Chinese)
(邓 楠,徐正光,王 珺.基于稀疏表征多分类器融合的遮挡人脸识别.计算机应用研究, 2013, 30(6): 1914-1916, 1920)
[12] Shaban A, Rabiee H R, Farajtabar M, et al. From Local Similarity to Global Coding: An Application to Image Classification // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Portland, USA, 2013: 2794-2801
[13] Tan H L, Yang B, Ma Z M. Face Recognition Based on the Fusion of Global and Local HOG Features of Face Images. IET Computer Vision, 2014, 8(3): 224-234
[14] Xu Y, Li X L, Yang J, et al. Integrating Conventional and Inverse Representation for Face Recognition. IEEE Trans on Cybernetics, 2014, 44(10): 1738-1746
[15] Shiau Y H, Chen C C. A Sparse Representation Method with Maximum Probability of Partial Ranking for Face Recognition // Proc of the 19th IEEE International Conference on Image Processing. Orlando, USA, 2012: 1445-1448 |
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2015, Vol. 28 
