  Detecting action units is an important task in face analysis, especially in
facial expression recognition. This is due, in part, to the idea that
expressions can be decomposed into multiple action units. In this paper we
investigate the impact of action unit occurrence patterns on detection of
action units. To facilitate this investigation, we review state of the art
literature, for AU detection, on 2 state-of-the-art face databases that are
commonly used for this task, namely DISFA, and BP4D. Our findings, from this
literature review, suggest that action unit occurrence patterns strongly impact
evaluation metrics (e.g. F1-binary). Along with the literature review, we also
conduct multi and single action unit detection, as well as propose a new
approach to explicitly train deep neural networks using the occurrence patterns
to boost the accuracy of action unit detection. These experiments validate that
action unit patterns directly impact the evaluation metrics.
