Facial landmark localization plays an important role in face recognition and analysis applications. In this paper, we give a brief introduction to a coarse-to-fine pipeline with neural networks and sequential regression. First, a global convolutional network is applied to the holistic facial image to give an initial landmark prediction. A pyramid of multi-scale local image patches is then cropped to feed to a new network for each landmark to refine the prediction. As the refinement network outputs a more accurate position estimation than the input, such procedure could be repeated several times until the estimation converges. We evaluate our system on the 300-W dataset [11] and it outperforms the recent state-of-the-arts.
@article{arxiv.1511.04901,
title = {Coarse-to-fine Face Alignment with Multi-Scale Local Patch Regression},
author = {Zhiao Huang and Erjin Zhou and Zhimin Cao},
journal= {arXiv preprint arXiv:1511.04901},
year = {2015}
}