Facial action unit (AU) detection, aiming to classify AU present in the
facial image, has long suffered from insufficient AU annotations. In this
paper, we aim to mitigate this data scarcity issue by learning AU
representations from a large number of unlabelled facial videos in a
contrastive learning paradigm. We formulate the self-supervised AU
representation learning signals in two-fold: (1) AU representation should be
frame-wisely discriminative within a short video clip; (2) Facial frames
sampled from different identities but show analogous facial AUs should have
consistent AU representations. As to achieve these goals, we propose to
contrastively learn the AU representation within a video clip and devise a
cross-identity reconstruction mechanism to learn the person-independent
representations. Specially, we adopt a margin-based temporal contrastive
learning paradigm to perceive the temporal AU coherence and evolution
characteristics within a clip that consists of consecutive input facial frames.
Moreover, the cross-identity reconstruction mechanism facilitates pushing the
faces from different identities but show analogous AUs close in the latent
embedding space. Experimental results on three public AU datasets demonstrate
that the learned AU representation is discriminative for AU detection. Our
method outperforms other contrastive learning methods and significantly closes
the performance gap between the self-supervised and supervised AU detection
approaches.

本研究提出了一种自监督对比学习方法，从大量未标注的面部视频中学习面部动作单元的表示，以解决缺乏数据的问题，并通过跨身份重建机制学习人物无关的表示。实验结果表明，所学习的面部动作单元表示对于动作单元检测具有较好的区分度，并且在自监督和有监督的动作单元检测方法之间显著减小了性能差距。