  Facial landmark localization aims to detect the predefined points of human
faces, and the topic has been rapidly improved with the recent development of
neural network based methods. However, it remains a challenging task when
dealing with faces in unconstrained scenarios, especially with large pose
variations. In this paper, we target the problem of facial landmark
localization across large poses and address this task based on a
split-and-aggregate strategy. To split the search space, we propose a set of
anchor templates as references for regression, which well addresses the large
variations of face poses. Based on the prediction of each anchor template, we
propose to aggregate the results, which can reduce the landmark uncertainty due
to the large poses. Overall, our proposed approach, named AnchorFace, obtains
state-of-the-art results with extremely efficient inference speed on four
challenging benchmarks, i.e. AFLW, 300W, Menpo, and WFLW dataset. Code will be
available at https://github.com/nothingelse92/AnchorFace.
