  The evolving algorithms for 2D facial landmark detection empower people to
recognize faces, analyze facial expressions, etc. However, existing methods
still encounter problems of unstable facial landmarks when applied to videos.
Because previous research shows that the instability of facial landmarks is
caused by the inconsistency of labeling quality among the public datasets, we
want to have a better understanding of the influence of annotation noise in
them. In this paper, we make the following contributions: 1) we propose two
metrics that quantitatively measure the stability of detected facial landmarks,
2) we model the annotation noise in an existing public dataset, 3) we
investigate the influence of different types of noise in training face
alignment neural networks, and propose corresponding solutions. Our results
demonstrate improvements in both accuracy and stability of detected facial
landmarks.
