SIAM Journal on Imaging Sciences, Volume 13, Issue 3, Page 1446-1466, January 2020.
The sparse representation-based classifier (SRC) has been developed and verified as having great potential for real-world face recognition. In this paper, we propose a discriminative projection and representation-based classification (DPRC) method to enhance the discriminant ability of the SRC. The proposed method first obtains a discriminative projection matrix not only maximizing the ratio of the distance within interclass over the distance within intraclass, but also minimizing the linear approximation error within intraclass. Then it maps the original data onto the discriminative space, and adopts an SRC method to obtain the final solution. An inexact augmented Lagrangian method of multiplier is proposed for finding the optimal representation vector in our framework, and a proximal alternating minimization method is adopted to the iteration subproblems of the proposed method. The proposed method is proven to have the subsequence convergence property. Experimental results on Yale, ORL, and AR face image databases demonstrate that, compared with some existing feature extraction methods based on the SRC, the proposed DPRC method is more efficient.

中文翻译：

---

基于判别投影和表示的人脸识别分类框架

SIAM影像科学杂志，第13卷，第3期，第1446-1466页，2020年1月。
基于稀疏表示的分类器（SRC）已被开发和验证，具有在现实世界中识别面部的巨大潜力。在本文中，我们提出了一种基于判别投影和基于表示的分类（DPRC）方法，以增强SRC的判别能力。所提出的方法首先获得一个判别式投影矩阵，不仅使类间距离与类内距离的比值最大化，而且使类内线性近似误差最小。然后将原始数据映射到判别空间，并采用SRC方法获得最终解。提出了一种不精确的增强拉格朗日乘子方法，用于在我们的框架中找到最佳表示向量，该方法的迭代子问题采用近端交替最小化方法。实践证明，该方法具有子序列收敛性。在Yale，ORL和AR面部图像数据库上的实验结果表明，与现有的一些基于SRC的特征提取方法相比，所提出的DPRC方法效率更高。