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Facial feature detection from facial images has attracted great attention inthe field of computer vision. It is a nontrivial task since the appearance andshape of the face tend to change under different conditions. In this paper, wepropose a hierarchical probabilistic model that could infer the true locationsof facial features given the image measurements even if the face is withsignificant facial expression and pose. The hierarchical model implicitlycaptures the lower level shape variations of facial components using themixture model. Furthermore, in the higher level, it also learns the jointrelationship among facial components, the facial expression, and the poseinformation through automatic structure learning and parameter estimation ofthe probabilistic model. Experimental results on benchmark databasesdemonstrate the effectiveness of the proposed hierarchical probabilistic model.

A Hierarchical Probabilistic Model for Facial Feature Detection