Representation analysis and synthesis of lip images using dimensionality reduction
Understanding facial expressions in image sequences is an easy task for humans. Some of us are capable of lipreading by interpreting the motion of the mouth. Automatic lipreading by a computer is a challenging task, with so far limited success. The inverse problem of synthesizing real looking lip movements is also highly non-trivial. Today, the technology to automatically generate an image series that imitates natural postures is far from perfect. We introduce a new framework for facial image representation, analysis and synthesis, in which we focus just on the lower half of the face, specifically the mouth. It includes interpretation and classification of facial expressions and visual speech recognition, as well as a synthesis procedure of facial expressions that yields natural looking mouth movements. Our image analysis and synthesis processes are based on a parametrization of the mouth configuration set of images. These images are represented as points on a two-dimensional flat manifold that enables us to efficiently define the pronunciation of each word and thereby analyze or synthesize the motion of the lips. We present some examples of automatic lips motion synthesis and lipreading, and propose a generalization of our solution to the problem of lipreading different subjects.
automatic lipreading image sequence processing speech synthesis multidimensional scaling dimension reduction locally linear embedding