Modern face alignment methods have become quite accurate at predicting the
locations of facial landmarks, but they do not typically estimate the
uncertainty of their predicted locations nor predict whether landmarks are
visible. In this paper, we present a novel framework for jointly predicting
landmark locations, associated uncertainties of these predicted locations, and
landmark visibilities. We model these as mixed random variables and estimate
them using a deep network trained with our proposed Location, Uncertainty, and
Visibility Likelihood (LUVLi) loss. In addition, we release an entirely new
labeling of a large face alignment dataset with over 19,000 face images in a
full range of head poses. Each face is manually labeled with the ground-truth
locations of 68 landmarks, with the additional information of whether each
landmark is unoccluded, self-occluded (due to extreme head poses), or
externally occluded. Not only does our joint estimation yield accurate
estimates of the uncertainty of predicted landmark locations, but it also
yields state-of-the-art estimates for the landmark locations themselves on
multiple standard face alignment datasets. Our method's estimates of the
uncertainty of predicted landmark locations could be used to automatically
identify input images on which face alignment fails, which can be critical for
downstream tasks.

本文提出了一种新颖的框架用于联合预测特征点位置、这些特征点位置的不确定性和特征点可见性，使用深度网络学习这些变量，通过我们提出的 Location, Uncertainty, and Visibility Likelihood (LUVLi) 损失进行估计。通过对 19000 张面部图像进行重新标记，我们的方法不仅能够准确预测特征点位置的不确定性，而且在多个标准的面部特征点数据集中也能得到最先进的预测结果，我们方法的预测可以被用来自动识别面部特征点无法准确估计的图像，这对于下游任务是至关重要的。