Facial Action Unit (AU) detection is a crucial task in affective computing and social robotics as it helps to identify emotions expressed through facial expressions. Anatomically, there are innumerable correlations between AUs, which contain rich information and are vital for AU detection. Previous methods used fixed AU correlations based on expert experience or statistical rules on specific benchmarks, but it is challenging to comprehensively reflect complex correlations between AUs via hand-crafted settings. There are alternative methods that employ a fully connected graph to learn these dependencies exhaustively. However, these approaches can result in a computational explosion and high dependency with a large dataset. To address these challenges, this paper proposes a novel self-adjusting AU-correlation learning (SACL) method with less computation for AU detection. This method adaptively learns and updates AU correlation graphs by efficiently leveraging the characteristics of different levels of AU motion and emotion representation information extracted in different stages of the network. Moreover, this paper explores the role of multi-scale learning in correlation information extraction, and design a simple yet effective multi-scale feature learning (MSFL) method to promote better performance in AU detection. By integrating AU correlation information with multi-scale features, the proposed method obtains a more robust feature representation for the final AU detection. Extensive experiments show that the proposed method outperforms the state-of-the-art methods on widely used AU detection benchmark datasets, with only 28.7\% and 12.0\% of the parameters and FLOPs of the best method, respectively. The code for this method is available at \url{https://github.com/linuxsino/Self-adjusting-AU}.

翻译：面部动作单元（AU）检测是情感计算与社会机器人学中的关键任务，有助于识别通过面部表情表达的情感。在解剖学上，AU之间存在无数相关性，这些相关性包含丰富的信息，对AU检测至关重要。以往方法基于专家经验或特定基准的统计规则使用固定的AU相关性，但通过手工设置难以全面反映AU之间的复杂关联。另有一类方法采用全连接图来详尽学习这些依赖关系，然而这些方法可能导致计算量爆炸且严重依赖大规模数据集。为解决这些问题，本文提出一种新颖的自调节AU相关性学习（SACL）方法，以较低的计算成本实现AU检测。该方法通过有效利用网络不同阶段提取的AU运动与情感表征信息特征，自适应地学习并更新AU相关性图。进一步地，本文探索了多尺度学习在相关性信息提取中的作用，并设计了一种简单而有效的多尺度特征学习（MSFL）方法，以提升AU检测性能。通过将AU相关性信息与多尺度特征相结合，所提方法为最终AU检测获得了更稳健的特征表征。大量实验表明，在广泛使用的AU检测基准数据集上，该方法性能优于当前最先进方法，且参数量与浮点运算量仅分别占最优方法的28.7%和12.0%。本方法代码开源于 \url{https://github.com/linuxsino/Self-adjusting-AU}。