  Face alignment algorithms locate a set of landmark points in images of faces
taken in unrestricted situations. State-of-the-art approaches typically fail or
lose accuracy in the presence of occlusions, strong deformations, large pose
variations and ambiguous configurations. In this paper we present 3DDE, a
robust and efficient face alignment algorithm based on a coarse-to-fine cascade
of ensembles of regression trees. It is initialized by robustly fitting a 3D
face model to the probability maps produced by a convolutional neural network.
With this initialization we address self-occlusions and large face rotations.
Further, the regressor implicitly imposes a prior face shape on the solution,
addressing occlusions and ambiguous face configurations. Its coarse-to-fine
structure tackles the combinatorial explosion of parts deformation. In the
experiments performed, 3DDE improves the state-of-the-art in 300W, COFW, AFLW
and WFLW data sets. Finally, we perform cross-dataset experiments that reveal
the existence of a significant data set bias in these benchmarks.
