Adjusting samples for obtaining better l₂-norm minimization based sparse representation

l₂-norm sparse representation (l₂-SR) based face recognition method has attracted increasing attention due to its excellent performance, simple algorithm and high computational efficiency. However, one of the drawbacks of l₂-SR is that the test sample may be conspicuous difference from the training samples even from the same class and thus the method shows poor robustness. Another drawback is that l₂-SR does not perform well in identifying the training samples that are trivial in correctly classifying the test sample. In this paper, to avoid the above imperfection, we proposed a novel l₂-SR. We first identifies the training samples that are important in correctly classifying the test sample and then neglects components that cannot be represented by the training samples. The proposed method also involve in-depth analysis of l₂-SR and provide novel ideas to improve previous methods. Experimental results on face datasets show that the proposed method can greatly improve l₂-SR.