Interpreting Face Images by Fitting a Fast Illumination-Based 3D Active Appearance Model

Abstract

We present a fast and robust iterative method for interpreting face images under non-uniform lighting conditions by using a fitting algorithm which utilizes an illumination-based 3D active appearance model in order to fit a face model to an input face image. Our method is based on improving the Jacobian each iteration using the parameters of lighting that have been estimated in preceding iterations. In the training stage, we precalculate a set of synthetic face images of basis reflectances and albedo generated from displacing one at the time, each one of the model parameters, and subsequently, in the fitting stage, we use all these images in combination with lighting parameters for assembling a Jacobian matrix adapted to the illumination estimated in the last iteration. In contrast to other works where an initial pose is required to begin the fit, our approach only uses a simple initialization in translation and scale. At the end of the fitting process, our algorithm obtains a compact set of parameters of albedo, 3D shape, 3D pose and illumination which describe the appearance of the input face image.