A Deeply-Initialized Coarse-to-fine Ensemble of Regression Trees for Face Alignment

Roberto Valle, José M. Buenaposada, Antonio Valdés, Luis Baumela

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 initial-
ization, self occlusions, and simultaneous frontal and profile face analysis.
In the experiments DCFE achieves the best reported result in AFLW,
COFW, and 300 W private and common public data sets.