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 300Â W private and common public data sets.
CITATION STYLE
Valle, R., Buenaposada, J. M., Valdés, A., & Baumela, L. (2018). A deeply-initialized coarse-to-fine ensemble of regression trees for face alignment. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11218 LNCS, pp. 609–624). Springer Verlag. https://doi.org/10.1007/978-3-030-01264-9_36
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