Fitting 3D face models for tracking and active appearance model training

In this paper, we consider fitting a 3D deformable face model to continuous video sequences for the tasks of tracking and training. We propose two appearance-based methods that only require a simple statistical facial texture model and do not require any information about an empirical or analytical gradient matrix, since the best search directions are estimated on the fly. The first method computes the fitting using a locally exhaustive and directed search where the 3D head pose and the facial actions are simultaneously estimated. The second method decouples the estimation of these parameters. It computes the 3D head pose using a robust feature-based pose estimator incorporating a facial texture consistency measure. Then, it estimates the facial actions with an exhaustive and directed search. Fitting and tracking experiments demonstrate the feasibility and usefulness of the developed methods. A performance evaluation also shows that the proposed methods can outperform the fitting based on an active appearance model search adopting a pre-computed gradient matrix. Although the proposed schemes are not as fast as the schemes adopting a directed continuous search, they can tackle many disadvantages associated with such approaches.