Face alignment consists in aligning a shape model on a face in an image. It is an active domain in computer vision as it is a preprocessing for applications like facial expression recognition, face recognition and tracking, face animation, etc. Current state-of-the-art methods already perform well on "easy" datasets, i.e. those that present moderate variations in head pose, expression, illumination or partial occlusions, but may not be robust to "in-the-wild" data. In this paper, we address this problem by using an ensemble of deep regressors instead of a single large regressor. Furthermore, instead of averaging the outputs of each regressor, we propose an adaptive weighting scheme that uses a tree-structured gate. Experiments on several challenging face datasets demonstrate that our approach outperforms the state-of-the-art methods.
@article{arxiv.1907.03248,
title = {Tree-gated Deep Regressor Ensemble For Face Alignment In The Wild},
author = {Estephe Arnaud and Arnaud Dapogny and Kevin Bailly},
journal= {arXiv preprint arXiv:1907.03248},
year = {2019}
}