  Although current face alignment algorithms have obtained pretty good
performances at predicting the location of facial landmarks, huge challenges
remain for faces with severe occlusion and large pose variations, etc. On the
contrary, semantic location of facial boundary is more likely to be reserved
and estimated on these scenes. Therefore, we study a two-stage but end-to-end
approach for exploring the relationship between the facial boundary and
landmarks to get boundary-aware landmark predictions, which consists of two
modules: the self-calibrated boundary estimation (SCBE) module and the
boundary-aware landmark transform (BALT) module. In the SCBE module, we modify
the stem layers and employ intermediate supervision to help generate
high-quality facial boundary heatmaps. Boundary-aware features inherited from
the SCBE module are integrated into the BALT module in a multi-scale fusion
framework to better model the transformation from boundary to landmark heatmap.
Experimental results conducted on the challenging benchmark datasets
demonstrate that our approach outperforms state-of-the-art methods in the
literature.
