Statistical active model for mouth components segmentation

Mouth segmentation is an important issue which applies in many multimedia applications as speech reading, face synthesis, recognition or audiovisual communication. In this paper, we propose a method based on a statistical model of shape and appearance to detect the lips. To create the model, the outline of the lips and teeth has to be manually annotated with 30 key-points on a few visemes (450). Once the model has been trained on this set, it is used for segmentation. After a step to situate mouth corners, the goal is to find the parameters to fit the model to an unknown image. The originalities of this work are (a) an initialization step which broadly classify lip and skin pixels, (b) the mouth corners local model, and (c) the automatically extracted dynamic and static sampled-appearance which are well adapted to describe the mouth area and its components.