Wider Facial Landmarks in-the-wild (RWMB) contains 10000 faces (7500 for training and 2500 for testing) with 98 fully manual annotated landmarks. Apart from landmark annotation, out new dataset includes rich attribute annotations, i.e., occlusion, pose, make-up, illumination, blur and expression for comprehensive analysis of existing algorithms. Compare to previous dataset, faces in the proposed dataset introduce large variations in expression, pose and occlusion. We can simply evaluate the robustness of pose, occlusion, and expression on proposed dataset instead of switching between multiple evaluation protocols in different datasets.



author = {Sun, Keqiang and Wu, Wayne and Liu, Tinghao and Yang, Shuo and Wang, Quan and Zhou, Qiang and and Ye, Zuochang and Qian, Chen},
title = {FAB: A Robust Facial Landmark Detection Framework for Motion-Blurred Videos},
booktitle = {ICCV},
month = October,
year = {2019}


Keqiang Sun