Image recognition with occlusion is one of the popular problems in pattern recognition. This paper partitions the images into some modules in two layers and the sparsity difference is used to evaluate the occluded modules. The final identification is processed on the unoccluded modules by sparse representation. Firstly, we partition the images into four blocks and sparse representation is performed on each block, so the sparsity of each block can be obtained; secondly, each block is partitioned again into two modules. Sparsity of each small module is calculated as the first step. Finally, the sparsity difference of small module with the corresponding block is used to detect the occluded modules; in this paper, the small modules with negative sparsity differences are considered as occluded modules. The identification is performed on the selected unoccluded modules by sparse representation. Experiments on the AR and Yale B database verify the robustness and effectiveness of the proposed method. 1. Introduction Image recognition, especially face recognition, has attracted a lot of researchers due to its wide application. And many methods have been proposed to solve this problem, including PCA, LDA, SVM, and other related methods. Recently, sparse representation- (or coding-) based classification (SRC) is attracting more and more attention [1–3] and has gained great success in face recognition. Based on sparse representation, Qiao et al. propose sparsity preserving projections (SPP) [4] for unsupervised dimensionality reduction. It can preserve the sparse reconstructive weights and the application on the face recognition verifies the effective SPP. Although these methods perform well under some controlled conditions, they fail to perform well in the situation when test data is corrupted due to occlusion. To solve this problem, Wanger et al. proposed to solve the problem in paper [5] by extending training samples using the difference between samples. And paper [6] used the image Gabor-features for SRC, which can get a more compact occlusion dictionary; as a result, the computation complexity and the number of atoms were reduced. In addition, paper [7] proposed a novel low-rank matrix approximation algorithm with structural incoherence for robust face recognition. In this paper the raw training data was decomposed into a low-rank matrix and the sparse error matrix. Besides, it introduced structural incoherence between low-rank matrices which promoted the discrimination between different classes, and thus this method exhibits excellent discriminating ability.
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