A Deeply-initialized Coarse-to-fine Ensemble of
Regression Trees for Face Alignment
Abstract. In this paper we present DCFE, a real-time facial landmark
regression method based on a coarse-to-fine Ensemble of Regression Trees
(ERT). We use a simple Convolutional Neural Network (CNN) to generate probability maps of landmarks location. These are further refined
with the ERT regressor, which is initialized by fitting a 3D face model
to the landmark maps. The coarse-to-fine structure of the ERT lets us
address the combinatorial explosion of parts deformation. With the 3D
model we also tackle other key problems such as robust regressor initialization, self occlusions, and simultaneous frontal and profile face analysis.
In the experiments DCFE achieves the best reported result in AFLW,
COFW, and 300W private and common public data sets.