Dictionary learning is one of the most important algorithms for face recognition. However, many dictionary learning algorithms for face recognition have the problems of small sample and weak discriminability. In this paper, a novel discriminative dictionary learning algorithm based on sample diversity and locality of atoms is proposed to solve the problems. The rational sample diversity is implemented by alternative samples and new error model to alleviate the small sample size problem. Moreover, locality can leads to sparsity and strong discriminability. In this paper, to enhance the dictionary discrimination and to reduce the influence of noise, the graph Laplacian matrix of atoms is used to keep the local information of the data. At the same, the relational theory is presented. A large number of experiments prove that the proposed algorithm can achieve more high performance than some state-of-the-art algorithms.

字典学习是人脸识别最重要的算法之一。然而，许多用于人脸识别的字典学习算法都存在样本量小和可分辨性差的问题。为了解决该问题，本文提出了一种基于样本多样性和原子局部性的判别词典学习算法。合理的样本多样性是通过替代样本和新的误差模型实现的，从而减轻了样本量小的问题。此外，局部性可能导致稀疏性和强烈的可辨别性。在本文中，为了增强字典的辨别力并减少噪声的影响，使用了原子的图拉普拉斯矩阵来保留数据的局部信息。同时，提出了关系理论。