A data-driven model for monocular face tracking

Abstract

This paper describes a two-stage system for 3D tracking of pose and deformation of the human face in monocular image sequences without the use of special markers. The first stage of the system learns the space of all possible facial deformations by applying principal component analysis on real stereo tracking data. The resulting model approximates any generic shape as a linear combination of shape basis vectors. The second stage of the system uses this low-complexity deformable model for simultaneous tracking of pose and deformation of the face from a single image sequence. This stage is known as model-based monocular tracking. There are three main contributions of this paper. First we demonstrate that a data-driven approach for model construction is suitable for tracking non rigid objects and offers an elegant and practical alternative to the task of manual construction of models using 3D scanners or CAD modelers. Second, we show that such a method exhibits good tracking accuracy (errors less than 5 mm) and robustness characteristics. Third, we demonstrate that our system exhibits very promising generalization properties in enabling tracking of multiple persons with the same 3D model.