Robust face recognition with illumination normalization using a reference profile

Illumination affects face recognition accuracy. When the face images are captured under uncontrolled environment, they can undergo uneven illumination due to many factors such as directional lighting, specular reflection, etc. The problem is well studied by researchers in multiple directions. We propose to solve the problem by systematically choosing a single face image as reference for computing reference probability density function and normalizing both training and test datasets of images with reference to it. We demonstrate superior performance of the proposed method and its generalization ability by conducting experiments on multiple face databases. The datasets offer challenges of varying illumination and shadows arising out of light sources placed at different angles of azimuth and elevation. We also show consistent performance of the scheme with different choices of reference images of varying illumination.