Facial Action Units Detection Using Temporal Context and Feature Reassignment

Facial action units (AUs) encode the activations of facial muscle groups, playing a crucial role in expression analysis and facial animation. However, current deep learning AU detection methods primarily focus on single-image analysis, which limits the exploitation of rich temporal context for robust outcomes. Moreover, the scale of available datasets remains limited, leading models trained on these datasets to tend to suffer from overfitting issues. This paper proposes a novel AU detection method integrating spatial and temporal data with inter-subject feature reassignment for accurate and robust AU predictions. Our method first extracts regional features from facial images. Then, to effectively capture both the temporal context and identity-independent features, we introduce a Temporal feature Combination and Feature Reassignment (TC&FR) module, which transforms single-image features into a cohesive temporal sequence and fuses features across multiple subjects. This transformation encourages the model to utilize identity-independent features and temporal context, thus ensuring robust prediction outcomes. Experimental results demonstrate the enhancements brought by the proposed modules and the state-of-the-art (SOTA) results achieved by our method.