This paper presents our Facial Action Units (AUs) recognition submission to
the fifth Affective Behavior Analysis in-the-wild Competition (ABAW). Our
approach consists of three main modules: (i) a pre-trained facial
representation encoder which produce a strong facial representation from each
input face image in the input sequence; (ii) an AU-specific feature generator
that specifically learns a set of AU features from each facial representation;
and (iii) a spatio-temporal graph learning module that constructs a
spatio-temporal graph representation. This graph representation describes AUs
contained in all frames and predicts the occurrence of each AU based on both
the modeled spatial information within the corresponding face and the learned
temporal dynamics among frames. The experimental results show that our approach
outperformed the baseline and the spatio-temporal graph representation learning
allows our model to generate the best results among all ablated systems. Our
model ranks at the 4th place in the AU recognition track at the 5th ABAW
Competition