This project addressed the challenge of improving face model fitting on video sequences.
Active Appearance Models (AAMs) represent the shape and appearance of an object via two low-dimensional subspaces, one for shape and one for appearance. AAMs for facial images are currently receiving considerable attention from the computer vision community; however, most existing work focuses on fitting AAMs to a single image. For many applications, effectively fitting an AAM to video sequences is of critical importance and challenging, especially considering the varying quality of real-world video content. This paper proposes a hybrid model to address this problem. Both a generic AAM and a subject-specific model are employed simultaneously in the proposed fitting scheme. Experimental results from outdoor surveillance video sequences demonstrate the improved image registration across video frames and faster fitting convergence. (Publisher abstract provided)