  Facial action units (AUs), as defined in the Facial Action Coding System
(FACS), have received significant research interest owing to their diverse
range of applications in facial state analysis. Current mainstream FAU
recognition models have a notable limitation, i.e., focusing only on the
accuracy of AU recognition and overlooking explanations of corresponding AU
states. In this paper, we propose an end-to-end Vision-Language joint learning
network for explainable FAU recognition (termed VL-FAU), which aims to
reinforce AU representation capability and language interpretability through
the integration of joint multimodal tasks. Specifically, VL-FAU brings together
language models to generate fine-grained local muscle descriptions and
distinguishable global face description when optimising FAU recognition.
Through this, the global facial representation and its local AU representations
will achieve higher distinguishability among different AUs and different
subjects. In addition, multi-level AU representation learning is utilised to
improve AU individual attention-aware representation capabilities based on
multi-scale combined facial stem feature. Extensive experiments on DISFA and
BP4D AU datasets show that the proposed approach achieves superior performance
over the state-of-the-art methods on most of the metrics. In addition, compared
with mainstream FAU recognition methods, VL-FAU can provide local- and
global-level interpretability language descriptions with the AUs' predictions.
