A visual analysis/synthesis feedback loop for accurate face tracking

Abstract

We propose a novel approach for face tracking, resulting in a visual feedback loop: instead of trying to adapt a more or less realistic artificial face model to an individual, we construct from precise range data a specific texture and wireframe face model, whose realism allows the analysis and synthesis modules to visually cooperate in the image plane, by directly using 2D patterns synthesized by the face model. Unlike other feedback loops found in the literature, we do not explicitly handle the 3D complex geometric data of the face model, to make real-time manipulations possible. Our main contribution is a complete face tracking and pose estimation framework, with few assumptions about the face rigid motion (allowing large rotations out of the image plane), and without marks or makeup on the user's face. Our framework feeds the feature-tracking procedure with synthesized facial patterns, controlled by an extended Kalman filter. Within this framework, we present original and efficient geometric and photometric modelling techniques, and a reformulation of a block-matching algorithm to make it match synthesized patterns with real images, and avoid background areas during the matching. We also offer some numerical evaluations, assessing the validity of our algorithms, and new developments in the context of facial animation. Our face-tracking algorithm may be used to recover the 3D position and orientation of a real face and generate a MPEG-4 animation stream to reproduce the rigid motion of the face with a synthetic face model. It may also serve as a pre-processing step for further facial expression analysis algorithms, since it locates the position of the facial features in the image plane, and gives precise 3D information to take into account the possible coupling between pose and expressions of the analysed facial images.