Expressions and facial action units (AUs) are two levels of facial behavior
descriptors. Expression auxiliary information has been widely used to improve
the AU detection performance. However, most existing expression representations
can only describe pre-determined discrete categories (e.g., Angry, Disgust,
Happy, Sad, etc.) and cannot capture subtle expression transformations like
AUs. In this paper, we propose a novel fine-grained \textsl{Global Expression
representation Encoder} to capture subtle and continuous facial movements, to
promote AU detection. To obtain such a global expression representation, we
propose to train an expression embedding model on a large-scale expression
dataset according to global expression similarity. Moreover, considering the
local definition of AUs, it is essential to extract local AU features.
Therefore, we design a \textsl{Local AU Features Module} to generate local
facial features for each AU. Specifically, it consists of an AU feature map
extractor and a corresponding AU mask extractor. First, the two extractors
transform the global expression representation into AU feature maps and masks,
respectively. Then, AU feature maps and their corresponding AU masks are
multiplied to generate AU masked features focusing on local facial region.
Finally, the AU masked features are fed into an AU classifier for judging the
AU occurrence. Extensive experiment results demonstrate the superiority of our
proposed method. Our method validly outperforms previous works and achieves
state-of-the-art performances on widely-used face datasets, including BP4D,
DISFA, and BP4D+.

本文提出了一种用于促进 AU 检测的新型细粒度全局表情表示编码器以及设计了用于为每个 AU 生成本地面部特征的本地 AU 特征模块。实验结果表明，我们的方法优于现有方法并在 BP4D、DISFA 和 BP4D + 等广泛使用的面部数据集上实现了最先进的性能。