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

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.