论文标题
考虑边界：融合地标热图回归的多层边界信息
Think about boundary: Fusing multi-level boundary information for landmark heatmap regression
论文作者
论文摘要
尽管目前的面部比对算法在预测面部标志的位置方面取得了相当不错的表现，但是对于严重的闭塞和较大的姿势变化等方面的面孔仍然存在巨大挑战。相反，面部边界的语义位置更可能在这些场景中保留和估计。因此，我们研究了一种两阶段但端到端的方法，用于探索面部边界与地标之间的关系，以获得边界吸引的地标预测，该预测由两个模块组成：自校准的边界估计（SCBE）模块和边界知识的地标变换（BALT）模块。在SCBE模块中，我们修改茎层并采用中间监督以帮助产生高质量的面部边界热图。从SCBE模块继承的边界感知特征在多尺度融合框架中集成到Balt模块中，以更好地模拟从边界到地标热图的转换。对具有挑战性的基准数据集进行的实验结果表明，我们的方法在文献中的表现优于最先进的方法。
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.
