Analysis and synthesis of facial image sequences in model-based image coding

Abstract
This paper proposes new methods for analyzing image sequences and updating textures of the three-dimensional (3-D) facial model. It also describes a method for synthesizing various facial expressions. These three methods are the key technologies for the model-based image coding system. The input image analysis technique directly and robustly estimates the 3-D head motions and the facial expressions without any two-dimensional (2-D) entity correspondences. This technique resolves the 2-D correspondence mismatch errors and provides quality reproduction of the original images by fully incorporating the synthesis rules. To verify the analysis algorithm, the paper performs quantitative and subjective evaluations. It presents two methods for updating the texture of the facial model to improve the quality of the synthesized images. The first method focuses on the facial parts with large change of brightness according to the various facial expressions for reducing the transmission bit rates. The second method focuses on all changes of brightness caused by the 3-D head motions as well as the facial expressions. The transmission bit rates are estimated according to the update methods. For synthesizing the output images, it describes rules that simulate the facial muscular actions because the muscles cause the facial expressions. These rules more easily synthesize the high-quality facial images that represent the various facial expressions

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