Deep Learning models based on heatmap regression have revolutionized the task
of facial landmark localization with existing models working robustly under
large poses, non-uniform illumination and shadows, occlusions and
self-occlusions, low resolution and blur. However, despite their wide adoption,
heatmap regression approaches suffer from discretization-induced errors related
to both the heatmap encoding and decoding process. In this work we show that
these errors have a surprisingly large negative impact on facial alignment
accuracy. To alleviate this problem, we propose a new approach for the heatmap
encoding and decoding process by leveraging the underlying continuous
distribution. To take full advantage of the newly proposed encoding-decoding
mechanism, we also introduce a Siamese-based training that enforces heatmap
consistency across various geometric image transformations. Our approach offers
noticeable gains across multiple datasets setting a new state-of-the-art result
in facial landmark localization. Code alongside the pretrained models will be
made available at this https URL

本文提出了一种新的基于连续分布的热图编码与解码方法以及引入 Siamese-based 训练策略，显著提高了面部特征定位的准确度，并在多个数据集上取得了新的最先进结果。