Face Alignment is an active computer vision domain, that consists in
localizing a number of facial landmarks that vary across datasets.
State-of-the-art face alignment methods either consist in end-to-end
regression, or in refining the shape in a cascaded manner, starting from an
initial guess. In this paper, we introduce DeCaFA, an end-to-end deep
convolutional cascade architecture for face alignment. DeCaFA uses
fully-convolutional stages to keep full spatial resolution throughout the
cascade. Between each cascade stage, DeCaFA uses multiple chained transfer
layers with spatial softmax to produce landmark-wise attention maps for each of
several landmark alignment tasks. Weighted intermediate supervision, as well as
efficient feature fusion between the stages allow to learn to progressively
refine the attention maps in an end-to-end manner. We show experimentally that
DeCaFA significantly outperforms existing approaches on 300W, CelebA and WFLW
databases. In addition, we show that DeCaFA can learn fine alignment with
reasonable accuracy from very few images using coarsely annotated data.

DeCaFA 是一种端到端的深度卷积级联体系结构，它使用全卷积阶段保持整个级联的完整空间分辨率，并利用多个链接转移层生成面部对齐任务的每个标记的基于注意力的地标注意力图。通过加权中间监督以及阶段之间的高效功能融合，DeCaFA 可以学习逐步以端到端的方式细化注意力图。结果表明，DeCaFA 在 300W，CelebA 和 WFLW 数据库上显着优于现有方法。此外，研究表明 DeCaFA 能够利用粗略注释数据从极少量图像中学习良好的对齐。