Illumination normalization for color face images

Abstract

The performance of appearance based face recognition algorithms is adversely affected by illumination variations. Illumination normalization can greatly improve their performance. We present a novel algorithm for illumination normalization of color face images. Face Albedo is estimated from a single color face image and its co-registered 3D image (pointcloud). Unlike existing approaches, our algorithm takes into account both Lambertian and specular reflections as well as attached and cast shadows. Moreover, our algorithm is invariant to facial pose and expression and can effectively handle the case of multiple extended light sources. The approach is based on Phong's lighting model. The parameters of the Phong's model and the number, direction and intensities of the dominant light sources are automatically estimated. Specularities in the face image are used to estimate the directions of the dominant light sources. Next, the 3D face model is ray-casted to find the shadows of every light source. The intensities of the light sources and the parameters of the lighting model are estimated by fitting Phong's model onto the skin data of the face. Experiments were performed on the challenging FRGC v2.0 data and satisfactory results were achieved (the mean fitting error was 6.3% of the maximum color value). © Springer-Verlag Berlin Heidelberg 2006.