Estimation of rigid and non-rigid facial motion using anatomical face model

We present a model-based approach to recover the rigid and non-rigid facial motion parameters in video sequences. Our face model is based on anatomically motivated muscle actuator controls to model the articulated non-rigid motion of a human face. The model is capable of generating a variety of facial expressions by using a small number of muscle actuator controls. We estimate rigid and non-rigid parameters in two steps. First, we use a multi-resolution scheme to recover the global 3D rotation and translation by linear least square minimization. Then, we estimate the muscle actuator controls using the Levenberg-Marquardt minimization technique applied to a Junction, which is constrained by both optical flow and the dynamics of the deformable model. We present the results of our system on both real and synthetic images.