An adaptive deformable template for mouth boundary modeling

The authors propose an algorithm to automatically extract mouth boundary model in human face images using deformable templates. Our vision algorithm is based on a hierarchical model adaptation scheme. In this paper it will be shown that the role of priori knowledge of the domain is essential for perceptual organization in our algorithm. The knowledge about the shape of the object is used to define its initial deformable template. Each mouth boundary curve is initially formed based on three control points whose locations are found through an optimization process using a suitable cost functional. The cost functional captures the essential knowledge about the shape to perceptually organize image information. Two of the control points are the mouth corners that are primarily located using the priori knowledge of the properties of edge map of the mouth image at its corners. They are used as the initial location of the mouth after an approximate mouth window is found based on locating the head boundary. The model is hierarchically improved in the second stage of the algorithm. Each boundary curve is finely tuned using more control points. An old model is adaptively replaced by a new model only if a secondary cost is further reduced. The results show that model adaptation technique satisfactorily enhances the mouth boundary model in an automated fashion.