Label Associated Dictionary Pair Learning for Face Recognition

Dictionary learning (DL) has been successfully applied to various pattern classification tasks. Sparse coding has played a vital role in the success of such DL-based models. However, the popular sparsity constraints using l(0) or l(1)-norm often make the training phase time-consuming. Recently, an emerging trend in using l(2)-norm has shown its advantages in both accuracy and computational speed. However, the supervised approach that exploits label information in the training phase has not been investigated in such l(2)-norm based methods. In this paper, we propose a novel supervised dictionary learning method that incorporates label information in the objective function. Based on that, we also propose an effective classification schema. Experiments on three popular face recognition datasets show that our method has promising results. Especially, our method has extremely fast speed in test phase, while maintaining competitive accuracy in comparison with other state-of-the-art models.