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. Code will be available soon.
|Publication status||Published - 2021|
|Event||2021 International Joint Conference on Neural Networks, IJCNN 2021 - Virtual, Shenzhen, China|
Duration: 18 Jul 2021 → 22 Jul 2021
|Conference||2021 International Joint Conference on Neural Networks, IJCNN 2021|
|Period||18/07/21 → 22/07/21|
ASJC Scopus subject areas
- Artificial Intelligence