Facial action unit (AU) detection and face alignment are two highly
correlated tasks since facial landmarks can provide precise AU locations to
facilitate the extraction of meaningful local features for AU detection. Most
existing AU detection works often treat face alignment as a preprocessing and
handle the two tasks independently. In this paper, we propose a novel
end-to-end deep learning framework for joint AU detection and face alignment,
which has not been explored before. In particular, multi-scale shared features
are learned firstly, and high-level features of face alignment are fed into AU
detection. Moreover, to extract precise local features, we propose an adaptive
attention learning module to refine the attention map of each AU adaptively.
Finally, the assembled local features are integrated with face alignment
features and global features for AU detection. Experiments on BP4D and DISFA
benchmarks demonstrate that our framework significantly outperforms the
state-of-the-art methods for AU detection.

本文提出了一种新颖的端到端深度学习框架以实现联合的面部动作单元检测和面部对齐任务，并通过在本地和全局特征中添加关注学习模块以提取精确的局部特征，在 BP4D 和 DISFA 基准测试上取得了显著的性能优势。