  Facial feature detection from facial images has attracted great attention in
the field of computer vision. It is a nontrivial task since the appearance and
shape of the face tend to change under different conditions. In this paper, we
propose a hierarchical probabilistic model that could infer the true locations
of facial features given the image measurements even if the face is with
significant facial expression and pose. The hierarchical model implicitly
captures the lower level shape variations of facial components using the
mixture model. Furthermore, in the higher level, it also learns the joint
relationship among facial components, the facial expression, and the pose
information through automatic structure learning and parameter estimation of
the probabilistic model. Experimental results on benchmark databases
demonstrate the effectiveness of the proposed hierarchical probabilistic model.
