  Subject-invariant facial action unit (AU) recognition remains challenging for
the reason that the data distribution varies among subjects. In this paper, we
propose a causal inference framework for subject-invariant facial action unit
recognition. To illustrate the causal effect existing in AU recognition task,
we formulate the causalities among facial images, subjects, latent AU semantic
relations, and estimated AU occurrence probabilities via a structural causal
model. By constructing such a causal diagram, we clarify the causal effect
among variables and propose a plug-in causal intervention module, CIS, to
deconfound the confounder \emph{Subject} in the causal diagram. Extensive
experiments conducted on two commonly used AU benchmark datasets, BP4D and
DISFA, show the effectiveness of our CIS, and the model with CIS inserted,
CISNet, has achieved state-of-the-art performance.
