论文标题
面部动作单元识别的基于注意力的关系网络
Attention Based Relation Network for Facial Action Units Recognition
论文作者
论文摘要
面部作用单元（AU）识别对于面部表达分析至关重要。由于AU之间存在高度正相关或负相关，因此一些现有的AU识别工作重点是建模AU关系。但是，以前基于关系的方法通常将预定义的规则嵌入其模型中，而忽略了不同人群中各种AU关系的影响。在本文中，我们提出了一个新型的基于注意力的关系网络（ABRNET），以供AU识别，该网络可以自动捕获AU的关系而无需不必要甚至令人不安的预定义规则。 ABRNET使用多个关系学习层自动捕获不同的AU关系。然后将学习的AU关系特征馈入自我发项的融合模块，该模块旨在优化具有注意力重量的单个AU特征，以增强功能鲁棒性。此外，我们提出了AU关系辍学策略和AU关系损失（AUR-LOSS），以更好地建模AU关系，这可以进一步改善AU的识别。广泛的实验表明，我们的方法在DISFA和DISFA+数据集上实现了最先进的性能。
Facial action unit (AU) recognition is essential to facial expression analysis. Since there are highly positive or negative correlations between AUs, some existing AU recognition works have focused on modeling AU relations. However, previous relationship-based approaches typically embed predefined rules into their models and ignore the impact of various AU relations in different crowds. In this paper, we propose a novel Attention Based Relation Network (ABRNet) for AU recognition, which can automatically capture AU relations without unnecessary or even disturbing predefined rules. ABRNet uses several relation learning layers to automatically capture different AU relations. The learned AU relation features are then fed into a self-attention fusion module, which aims to refine individual AU features with attention weights to enhance the feature robustness. Furthermore, we propose an AU relation dropout strategy and AU relation loss (AUR-Loss) to better model AU relations, which can further improve AU recognition. Extensive experiments show that our approach achieves state-of-the-art performance on the DISFA and DISFA+ datasets.
