Facial expression recognition via weighted group sparsity

Considering the distinctiveness of different group features in the sparse representation, a novel joint multi-task and weighted group sparsity (JMT-WGS) method is proposed. By weighting popular group sparsity, not only the representation coefficients from the same class over their associate dictionaries may share some similarity, but also the representation coefficients from different classes have enough diversity. The proposed method is cast into a multi-task framework with two-stage iteration. In the first stage, representation coefficient can be optimized by accelerated proximal gradient method when the weights are fixed. In the second stage, the weights are computed via the prior information about their entropy. The experimental results on three facial expression databases show that the proposed algorithm outperforms other state-of-the-art algorithms and demonstrate the promising performance of the proposed algorithm.