FAB: A Robust Facial Landmark Detection Framework for Motion-Blurred Videos

Keqiang Sun, Wenyan (Wayne) Wu, Tinghao Liu, Shuo Yang, Quan Wang, Qiang Zhou, Zuochang Ye, Chen Qian

Abstract:

Recently, facial landmark detection algorithms have
achieved remarkable performance on static images. However, these algorithms are neither accurate nor stable in
motion-blurred videos. The missing of structure information makes it difficult for state-of-the-art facial landmark
detection algorithms to yield good results.
In this paper, we propose a framework named FAB
that takes advantage of structure consistency in the temporal dimension for facial landmark detection in motionblurred videos. A structure predictor is proposed to
predict the missing face structural information temporally, which serves as a geometry prior. This allows
our framework to work as a virtuous circle. On one
hand, the geometry prior helps our structure-aware deblurring network generates high quality deblurred images
which lead to better landmark detection results. On the
other hand, better landmark detection results help structure predictor generate better geometry prior for the next
frame. Moreover, it is a flexible video-based framework
that can incorporate any static image-based methods to
provide a performance boost on video datasets. Extensive experiments on Blurred-300VW, the proposed Realworld Motion Blur (RWMB) datasets and 300VW demonstrate the superior performance to the state-of-the-art methods. Datasets and models will be publicly available at
https://keqiangsun.github.io/projects/FAB/FAB.html.