  Facial feature tracking is an active area in computer vision due to its
relevance to many applications. It is a nontrivial task, since faces may have
varying facial expressions, poses or occlusions. In this paper, we address this
problem by proposing a face shape prior model that is constructed based on the
Restricted Boltzmann Machines (RBM) and their variants. Specifically, we first
construct a model based on Deep Belief Networks to capture the face shape
variations due to varying facial expressions for near-frontal view. To handle
pose variations, the frontal face shape prior model is incorporated into a
3-way RBM model that could capture the relationship between frontal face shapes
and non-frontal face shapes. Finally, we introduce methods to systematically
combine the face shape prior models with image measurements of facial feature
points. Experiments on benchmark databases show that with the proposed method,
facial feature points can be tracked robustly and accurately even if faces have
significant facial expressions and poses.
