Illumination-Insensitive Face Recognition Using Symmetric Shape-from-Shading

Wen-Yi Zhao, Rama Chellappa

Abstract: Sensitivity to variations in illumination is a fundamental and challenging problem in face recognition. In this paper, we describe a new method based on symmetric shape-from-shading (SSFS) to develop a face recognition system that is robust to changes in illumination. The basic idea of this approach is to use the SSFS algorithm as a tool to obtain a prototype image which is illumination-normalized. It has been shown that the SSFS algorithm has a unique point-wise solution. But it is still difficult to recover accurate shape information given a single real face image with complex shape and varying albedo. In stead, we utilize the fact that all faces share a similar shape making the direct computation of the prototype image from a given face image feasible. Finally, to demonstrate the efficacy of our method, we have applied it to several publicly available face databases.