Lighting Normalization with Generic Intrinsic Illumination Subspace for Face Recognition

Chia-Ping Chen, Chu-Song Chen

Abstract: In this paper, we introduce the concept of intrinsic illumination subspace which is based on the intrinsic images. This intrinsic illumination subspace enables an analytic generation of the illumination images under varying lighting conditions. When objects of the same class are concerned, our method allows a class-based generic intrinsic illumination subspace to be constructed in advance. We propose a lighting normalization method based on the generic intrinsic illumination subspace, which is used as a bootstrap subspace for novel images. Face recognition experiments are performed to demonstrate the effectiveness of our method.