Joint Reflectance Field Estimation and Sparse Representation for Face Image Illumination Preprocessing and Recognition

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.

Automated summary

The study introduces a unified framework for face image illumination preprocessing and recognition, proposing a JRSR method for face recognition under extreme lighting conditions. The method effectively separates the identity factor and interfered illumination, improving recognition accuracy and preprocessing efficiency.