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

Roberto Valle, Jose M. Buenaposada, Antonio Valdes, Luis Baumela

ECCV 2018

doi:10.1007/978-3-030-01264-9_36 /eccv/2018/valle2018eccv-deeplyinitialized/

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.

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Text

Valle et al. "A Deeply-Initialized Coarse-to-Fine Ensemble of Regression Trees for Face Alignment." Proceedings of the European Conference on Computer Vision (ECCV), 2018. doi:10.1007/978-3-030-01264-9_36

Markdown

[Valle et al. "A Deeply-Initialized Coarse-to-Fine Ensemble of Regression Trees for Face Alignment." Proceedings of the European Conference on Computer Vision (ECCV), 2018.](https://mlanthology.org/eccv/2018/valle2018eccv-deeplyinitialized/) doi:10.1007/978-3-030-01264-9_36

BibTeX

@inproceedings{valle2018eccv-deeplyinitialized,
  title     = {{A Deeply-Initialized Coarse-to-Fine Ensemble of Regression Trees for Face Alignment}},
  author    = {Valle, Roberto and Buenaposada, Jose M. and Valdes, Antonio and Baumela, Luis},
  booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
  year      = {2018},
  doi       = {10.1007/978-3-030-01264-9_36},
  url       = {https://mlanthology.org/eccv/2018/valle2018eccv-deeplyinitialized/}
}