Description: 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 s show that with the proposed
method, facial feature points can be tracked robustly and
accurately even if faces have significant facial expressions
and poses.
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