A New Method for Face Alignment under Extreme Poses and Occlusion

摘要

In real-world conditions, robust face alignment is challenging due to the large variability of occlusion and pose. Many methods aim to solve the problem, but can handle either images with occlusion only or with arbitrary poses only. In this paper, we propose a unified framework by ignoring the points which cannot be seen under occlusion and extreme poses, in which we get facial parts first by classification and then train regression models to get key points. It leads to higher accuracy when locating the truly existing points without considering the occluded and non-existent points. Besides, we observed that the drift and shape of face detection results affect face alignment. As far as we know, we are the first to explicitly raise the issue and solve it to some extent. Finally, our method outperforms the state-of-the-art methods on AFLW and COFW datasets. It is also comparable to other methods on LFPW dataset.