Real-time estimation of long-term 3-D motion parameters for SNHC face animation and model-based coding applications

We present two recursive methods for the real-time estimation of long-term three-dimensional (3-D) motion parameters from monocular image sequences suitable for synthetic/natural hybrid coding face animation and model-based coding applications. Based on feature point extractions in energy frame, the 3-D motion parameters of a human face are estimated with a predictive approach. The first method uses a recursive linear least squares approach and the second employs a nonlinear extended Kalman filter, which does not rely on a linearized model of the face motion. Both methods perform a prediction and correction loop at every time step. Compared to other methods described in the literature, the recursive and predictive structure of the proposed estimation process solves the p