3D Facial Geometry Recovery via Group-wise Optical Flow

We describe an algorithm for automatically finding correspondences from face 
video sequences. This method is useful to many applications such as face 
tracking, face modeling and 3D face recovery. Given a sequence of images, the 
face feature points are tracked by a model-constraint optical flow algorithm. 
By employing a Minimum Description Length (MDL) framework, the drift-off error 
caused by the optical flow algorithm can be reduced and the correspondences 
can be matched robustly by optimizing the statistical model. As a result, the 
face is able to be tracked precisely. Furthermore, it offers a new method to 
build the appearance model automatically. We use the objective root mean square 
error (RMSE) to prove the efficiency of our proposed algorithm. At the same 
time, we evaluate the performance subjectively by generating 3D face models 
based on it.