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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2014, Vol. 27 
