  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.
