  This paper presents a new method to track both the face pose and the face
animation with a monocular camera. The approach is based on the 3D face model
CANDIDE and on the SIFT (Scale Invariant Feature Transform) descriptors,
extracted around a few given landmarks (26 selected vertices of CANDIDE model)
with a Bayesian approach. The training phase is performed on a synthetic
database generated from the first video frame. At each current frame, the face
pose and animation parameters are estimated via a Bayesian approach, with a
Gaussian prior and a Gaussian likelihood function whose the mean and the
covariance matrix eigenvalues are updated from the previous frame using eigen
decomposition. Numerical results on pose estimation and landmark locations are
reported using the Boston University Face Tracking (BUFT) database and Talking
Face video. They show that our approach, compared to six other published
algorithms, provides a very good compromise and presents a promising
perspective due to the good results in terms of landmark localization.
