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| Robust Occlusion Pattern Recognition Algorithm Based on Block Sparse Recursive Residuals Analysis |
| HU Zheng-Ping, ZHAO Shu-Huan, LI Jing |
| College of Information Science and Engineering, Yanshan University, Qinhuangdao 066004 |
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Abstract A robust occlusion pattern recognition algorithm is proposed which considers how to detect occluded region automatically with unknown occlusion pattern and conquers the influence of occluded region to improve the robustness of the recognition algorithm. Firstly, the test image is divided into up module and down module. Next, the sparse representations are computed respectively. Then, the module with higher sparsity and the corresponding sparse solution are found. The test image is reconstructed using this module and the N largest coefficients. According to the residual of original test image and reconstruction image, the occluded pixels can be confirmed coarsely. Considering the continuity of occluded region, the coarsely confirmed occluded region is regularized by morphological operation and gets the weighting matrix. Finally, the test and training set are weighted and normalized by using this weighting matrix and then the final decision is made by using global sparse representation. The experimental results on AR, Yale B and MNIST databases verify that the proposed method can detect the occluded region roughly, and the effectiveness and the robustness of the proposed method can be observed obviously.
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Received: 05 January 2013
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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] Zhang Lei, Yang Meng, Feng Xiangchu. Sparse Representation or Collaborative Representation: Which Helps Face Recognition? // Proc of the IEEE International Conference on Computer Vision. Barcelona, Spain, 2011: 471-478
[3] Xu Yong, Zuo Wangmeng, Fan Zizhu. Supervised Sparse Representation Method with a Heuristic Strategy and Face Recognition Experiments. Neurocomputing, 2012, 79(1): 125-131
[4] Wagner A,Wright J, Ganesh A, et al.Towards a Practical Face Recognition System: Robust Registration and Illumination by Sparse Representation // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Miami, USA, 2009: 597-604
[5] Yang Meng, Zhang Lei, Zhang D. Efficient Misalignment-Robust Representation for Real-Time Face Recognition // Proc of the 12th European Conference on Computer Vision. Firenze, Italy, 2012: 850-863
[6] Yang Meng, Zhang Lei. Gabor Feature Based Sparse Representation for Face Recognition with Gabor Occlusion Dictionary // Proc of the 11th European Conference on Computer Vision. Heraklion, Greece, 2010: 448-461
[7] Chen C F, Wei C P, Wang Y C F. Low-Rank Matrix Recovery with Structural Incoherence for Robust Face Recognition // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA, 2012: 2618-2625
[8] Chen Zhaohua, Xu Tingrong , Han Zhiyuan. Occluded Face Recognition Based on the Improved SVM and Block Weighted LBP // Proc of the International Conference on Image Analysis and Signal Processing. Hangzhou, China, 2011: 118-122
[9] Yang Meng, Zhang D, Jian Yang, et al. Robust Sparse Coding for Face Recognition // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Colorado Springs, USA, 2011: 625-632
[10] Lai Jian, Jiang Xudong. Modular Weighted Global Sparse Representation for Robust Face Recognition. IEEE Signal Processing Letters, 2012, 19(9): 571-574
[11] Yuk J S C, Wong K Y K, Chung R H Y. A Multi-Level Supporting Scheme for Face Recognition under Partial Occlusions and Disguise // Proc of the 10th Asian Conference on Computer Vision. Queenstown, New Zealand, 2010: 690-701
[12] Andres A M, Padovani S, Tepper M, et al. Randomized Face Recognition on Partially Occluded Images // Proc of the 17th Iberoamerican Congress on Pattern Recognition. Buenos Aires, Argentina, 2012: 90-97
[13] Venkata I, Khadera A T, Subramaniana K G, et al. Recognizing Occluded Faces by Exploiting Psychophysically Inspired Similarity Maps. Pattern Recognition Letters, 2013, 34(8): 903-911
[14] Li Xiaoxin, Dai Daoqing, Zhang Xiaofei, et al. Face Recognition with Continuous Occlusion Using Partially Iteratively Reweighted Sparse Coding // Proc of the 1st Asian Conference on Pattern Recognition. Beijing, China, 2011: 293-297
[15] Wang Zhiming, Tao Jianhua. Face Occlusion Detection and Reconstruction. Journal of Computer Research and Development, 2010, 47(1): 16-22 (in Chinese)
(王志明,陶建华.人脸遮挡区域检测与重建.计算机研究与发展, 2010, 47(1): 16-22) |
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2014, Vol. 27 
