Facial model adaptation from a monocular image sequence using a textured polygonal model

摘要

Although several algorithms have been proposed for facial model adaptation from image sequences, the insufficient feature set to adapt a full facial model, imperfect matching of feature points, and imprecise head motion estimation may degrade the accuracy of model adaptation. In this paper, we propose to resolve these difficulties by integrating facial model adaptation, texture mapping, and head pose estimation as cooperative and complementary processes. By using an analysis-by-synthesis approach, salient facial feature points and head profiles are reliably tracked and extracted to form a growing and more complete feature set for model adaptation. A more robust head motion estimation is achieved with the assistance of the textured facial model. The proposed scheme is performed with image sequences acquired with single uncalibrated camera and requires only little manual adjustment in the initialization setup, which proves to be a feasible approach for facial model adaptation.