Recently, deep learning based facial landmark detection has achieved great
success. Despite this, we notice that the semantic ambiguity greatly degrades
the detection performance. Specifically, the semantic ambiguity means that some
landmarks (e.g. those evenly distributed along the face contour) do not have
clear and accurate definition, causing inconsistent annotations by annotators.
Accordingly, these inconsistent annotations, which are usually provided by
public databases, commonly work as the ground-truth to supervise network
training, leading to the degraded accuracy. To our knowledge, little research
has investigated this problem. In this paper, we propose a novel probabilistic
model which introduces a latent variable, i.e. the 'real' ground-truth which is
semantically consistent, to optimize. This framework couples two parts (1)
training landmark detection CNN and (2) searching the 'real' ground-truth.
These two parts are alternatively optimized: the searched 'real' ground-truth
supervises the CNN training; and the trained CNN assists the searching of
'real' ground-truth. In addition, to recover the unconfidently predicted
landmarks due to occlusion and low quality, we propose a global heatmap
correction unit (GHCU) to correct outliers by considering the global face shape
as a constraint. Extensive experiments on both image-based (300W and AFLW) and
video-based (300-VW) databases demonstrate that our method effectively improves
the landmark detection accuracy and achieves the state of the art performance.

本文提出一种基于深度学习的新型概率模型，解决人脸关键点检测中存在的语义模糊问题，并提出了全局热力图校正单元进行精确定位，并通过图像和视频数据库的实验证明本文方法能够显著提高人脸关键点检测的精确性。