A video coding method called model-based image coding has attracted much attention as a potential candidate for low bit-rate visual communication services. This technique reconstructs the facial image with a preknown 3D human face model and its received model motion parameters. The parameters of the head motion are mainly divided into two parts: global motion parameters describe the rigid movement of the head, such as rotation and translation, and local motion parameters which deal with the nonrigid movements of facial expressions, such as the opening and closing of the mouth and eyes. In this paper, we propose a new approach which can estimate the head global motion more robustly and accurately. Comparing with the existing techniques to match only a few key points, here we extract 3-D contour feature points and use chamfer distance matching to estimate head global motion. This can improve and enhance the contour tracking performance greatly. We also develop another technique called facial normalization transform. It maps the facial region of the current input frame back to the normalized pose of the initial frame. Using this transform, we can analyze facial expressions at the same orientation and fixed region. This simplifies the analysis work a lot. Then, we do our encoding by the clip-and-paste method along with adaptive codebook technique. In the following the coder and decoder system are briefly described.
['Soo-Chang Pei', 'Ching-Wen Ko', 'Ming-Shing Su']
Global motion estimation in model-based image coding by tracking three-dimensional contour feature points