Cylindrical Model-Based Head Tracking and 3D Pose Recovery from Sequential Face Images

This paper presents a novel approach to estimate 3D head pose dynamically from a sequence of input images. The exact head pose estimation and facial motion tracking are critical problems to be solved in developing a vision based human computer interaction system. Given an initial reference template of head image and corresponding head pose, the full head motion is recovered by using a cylindrical head model. By updating the template dynamically in order to accommodate gradual changes in lighting, it is possible to recover head pose robustly regardless of light variation and self-occlusion. For this, we adopt optical flow along with iteratively re-weighted least square technique. From the experiments, we can show the proposed approach efficiently estimate 3D head pose