The Best of Both Worlds: Combining 3D Deformable Models with Active Shape Models

Reliable 3D tracking is still a difficult task. Most parametrized 3D deformable models rely on the accurate extraction of image features for updating their parameters, and are prone to failures when the underlying feature distribution assumptions are invalid. Active Shape Models (ASMs), on the other hand, are based on learning, and thus require fewer reliable local image features than parametrized 3D models, but fail easily when they encounter a situation for which they were not trained. In this paper, we develop an integrated framework that combines the strengths of both 3D deformable models and ASMs. The 3D model governs the overall shape, orientation and location, and provides the basis for statistical inference on both the image features and the parameters. The ASMs, in contrast, provide the majority of reliable 2D image features over time, and aid in recovering from drift and total occlusions. The framework dynamically selects among different ASMs to compensate for large viewpoint changes due to head rotations. This integration allows the robust tracking effaces and the estimation of both their rigid and non- rigid motions. We demonstrate the strength of the framework in experiments that include automated 3D model fitting and facial expression tracking for a variety of applications, including sign language.