A study on illumination normalization for 2D face verification

Illumination normalization is very important for 2D face verification. This study examines the state-of-art illumination normalization methods, and proposes two solutions, namely horizontal Gaussian derivative filters and local binary patterns. Experiments show that our methods significantly improve the generalization capability, while maintaining good discrimination capability of a face verification system. The proposed illumination normalization methods have low requirements on image acquisition, and low computation complexities, and are very suitable for low-end 2D face verification systems.