An antinoise sparse representation method for robust face recognition via joint l1 and l2 regularization

L1 or L2 regularization based representation is not antinoise enough.An antinoise sparse representation via joint L1 and L2 is proposed.The rationale of the objective function for fusion is analyzed.Recognition of noisy samples is evaluated as true positive.The Anti-L1L2 outperforms some state-of-art sparse algorithms. Sparse representation methods based on l1 and/or l2 regularization have shown promising performance in different applications. Previous studies show that the l1 regularization based representation has more sparse property, while the l2 regularization based representation is much simpler and faster. However, when dealing with noisy data, both naive l1 and l2 regularization suffer from the issue of unsatisfactory robustness. In this paper, we explore the method to implement an antinoise sparse representation method for robust face recognition based on a joint version of l1 and l2 regularization. The contributions of this paper are mainly shown in the following aspects. First, a novel objective function combining both l1 and l2 regularization is proposed to implement an antinoise sparse representation. An iterative fitting operation via l1 regularization is integrated with l2 norm minimization, to obtain an antinoise classification. Second, the rationale how the proposed method produces promising discriminative and antinoise performance for face recognition is analyzed. The l2 regularization enhances robustness and runs fast, and l1 regularization helps cope with the noisy data. Third, the classification robustness of the proposed method is demonstrated by extensive experiments on several benchmark facial datasets. The method can be considered as an option for the expert systems for biometrics and other recognition problems facing unstable and noisy data.