This paper describes a novel system for 3D head tracking under partial occlusion from 2D monocular image sequences. In this system, The Extended Superquadric (ESQ) is used to generate a geometric 3D face model in order to reduce the shape ambiguity. Optical flow is then employed with this model to estimate the 3D rigid motion. To deal with occlusion, a new motion segmentation algorithm using motion residual error analysis is developed. The occluded areas are successfully detected and discarded as noise by the system. Also, a new post-regularization process based on edge flow heavily reduces accumulation error. This makes the system more stable over long occlusion image sequences. To show the accuracy, the system is applied on a synthetic occlusion sequence and comparisons with the ground truth are reported. To show the robustness, experiments on long occlusion image sequences, including synthetic and real ones, are reported.