LAUSR

Since image set classification has strong power to overcome various variations in illumination, expression, pose, and so on, it has drawn extensive attention in recent years. Noteworthily, the point-to-point distance-based methods have achieved the promising performance, which aim to compute the similarity between each gallery set and the probe set for classification purpose. Nevertheless, these existing methods have to face the following problems: (1) they do not take full advantage of the between-set discrimination information; (2) they ideally presume that the importance of different gallery sets is equal, whereas this always violates objective facts and may degenerate algorithm performance in practice; (3) they tend to have high computational cost and several parameters, though explicit sparsity can enhance discrimination. To address these problems, we propose a novel method for face image set classification, namely self-weighted latent sparse discriminative learning (SLSDL). Specifically, a novel self-weighted strategy guided discrimination term is proposed to largely boost the discrimination of different gallery sets, such that the effect of true sets can be boosted while the effect of false sets can be weakened or removed. Moreover, we propose a latent sparse normalization to reduce computational complexity as well as the number of trade-off parameters. In addition, we propose an efficient optimization algorithm to solve the final SLSDL. Comprehensive experiments on four public benchmark datasets demonstrate that SLSDL is superior to the state-of-the-art competitors.