Data
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.
Figure 3: Example of a monolithic ERT regressor vs our coarse-to-fine approach
Citation
Roberto Valle and José Miguel Buenaposada and Antonio Valdés and Luis Baumela. A Deeply-Initialized Coarse-to-fine Ensemble of Regression Trees for Face Alignment. European Conference on Computer Vision 11218: 609-624 (2018)
@inproceedings{Valle18,
author = {Roberto Valle and Jos{\'{e}} Miguel Buenaposada and Antonio Vald{\'{e}}s and Luis Baumela},
title = {A Deeply-Initialized Coarse-to-fine Ensemble of Regression Trees for Face Alignment},
booktitle = {European Conference on Computer Vision},
volume = {11218},
pages = {609-624},
year = {2018},
url = {https://doi.org/10.1007/978-3-030-01264-9\_36}
}