Abstract
Current AU datasets lack sufficiency and diversity because annotating facial action units (AUs) is laborious. The lack of labeled AU datasets bottlenecks the training of a discriminative AU detector. Compared with AUs, the basic emotional categories are relatively easy to annotate and they are highly correlated to AUs. To this end, we propose an Emotion-aware Contrastive Learning (EmoCo) framework to obtain representations that retain enough AU-related information. EmoCo leverages enormous and diverse facial images without AU annotations while labeled with the six universal facial expressions. EmoCo extends the prevalent self-supervised learning architecture of Momentum Contrast by simultaneously classifying the learned features into different emotional categories and distinguishing features within each emotional category in instance level. In the experiments, we train EmoCo using AffectNet dataset labeled with emotional categories. The EmoCo-learned features outperform other self-supervised learned representations in AU detection tasks on DISFA, BP4D, and GFT datasets. The EmoCo-pretrained models that finetuned on the AU datasets outperform most of the state-of-the-art AU detection methods.
