Deep learning technique has dramatically boosted the performance of face
alignment algorithms. However, due to large variability and lack of samples,
the alignment problem in unconstrained situations, \emph{e.g}\onedot large head
poses, exaggerated expression, and uneven illumination, is still largely
unsolved. In this paper, we explore the instincts and reasons behind our two
proposals, \emph{i.e}\onedot Propagation Module and Focal Wing Loss, to tackle
the problem. Concretely, we present a novel structure-infused face alignment
algorithm based on heatmap regression via propagating landmark heatmaps to
boundary heatmaps, which provide structure information for further attention
map generation. Moreover, we propose a Focal Wing Loss for mining and
emphasizing the difficult samples under in-the-wild condition. In addition, we
adopt methods like CoordConv and Anti-aliased CNN from other fields that
address the shift-variance problem of CNN for face alignment. When implementing
extensive experiments on different benchmarks, \emph{i.e}\onedot WFLW, 300W,
and COFW, our method outperforms state-of-the-arts by a significant margin. Our
proposed approach achieves 4.05\% mean error on WFLW, 2.93\% mean error on 300W
full-set, and 3.71\% mean error on COFW.

本研究基于热图回归，通过将地标热图传播到边界热图，提供更多结构信息来解决大头姿势、夸张表情和不均匀光照等情况下的面部对齐问题，同时提出了 Focal Wing Loss 以优化野外条件下难处理的样本。该方法在 WFLW、300W 和 COFW 数据集上表现均优于现有技术，平均误差分别为 4.05%、2.93% 和 3.71%。