  Face alignment consists in aligning a shape model on a face in an image. It
is an active domain in computer vision as it is a preprocessing for
applications like facial expression recognition, face recognition and tracking,
face animation, etc. Current state-of-the-art methods already perform well on
"easy" datasets, i.e. those that present moderate variations in head pose,
expression, illumination or partial occlusions, but may not be robust to
"in-the-wild" data. In this paper, we address this problem by using an ensemble
of deep regressors instead of a single large regressor. Furthermore, instead of
averaging the outputs of each regressor, we propose an adaptive weighting
scheme that uses a tree-structured gate. Experiments on several challenging
face datasets demonstrate that our approach outperforms the state-of-the-art
methods.
