Illumination preprocessing is an important ingredient for handling lighting variation face recognition challenge. Nonetheless, existing methods are usually designed to be independent of the face recognition methods and the interaction between them is not yet well explored. In this paper, we formulate the face image illumination preprocessing and recognition into a unified sparse representation framework and propose a novel joint reflectance field estimation and sparse representation (JRSR) method for face recognition under extreme lighting conditions. The proposed method separates the identify factor and the interfered illumination of a query sample simultaneously by one nonconvex sparse optimizing model. We also present an efficient approximation algorithm to solve JRSR in this paper. Evaluation on several face databases and the experimental results of face recognition with illumination variation clearly demonstrate the advantages of our proposed JRSR algorithm in illumination preprocessing efficiency and recognition accuracy.

照明预处理是应对照明变化面部识别挑战的重要组成部分。然而，现有方法通常被设计为独立于面部识别方法，并且它们之间的相互作用还没有得到很好的探索。在本文中，我们将面部图像照明的预处理和识别公式化为统一的稀疏表示框架，并提出了一种新的联合反射场估计和稀疏表示（JRSR）方法，用于在极端光照条件下进行面部识别。所提出的方法通过一个非凸稀疏优化模型同时分离了识别因子和查询样本的干扰光照。我们还提出了一种有效的近似算法来解决JRSR问题。