Multi-attribute sparse representation with group constraints for face recognition under different variations

A novel multi-attribute sparse representation enforced with group constraints is proposed in this paper. Data with multiple attributes can be represented by individual binary matrices to indicate the group properties for each data sample. Then, these attribute matrices are incorporated into the formulation of l₁-minimization. The solution is obtained by jointly considering the data reconstruction error, the sparsity property as well as the group constraints, thus making the basis selection in sparse coding more efficient in term of accuracy. The proposed optimization formulation with group constraints is simple yet very efficient for classification problems with multiple attributes. In addition, it can be derived into a modified sparse coding form so that any l₁-minimization solver can be employed in the corresponding optimization problem. We demonstrate the performance of the proposed multi-attribute sparse representation algorithm through experiments on face recognition with different kinds of variations. Experimental results show that the proposed method is very competitive compared to the state-of-the-art methods.