  Facial action units allow an objective, standardized description of facial
micro movements which can be used to describe emotions in human faces.
Annotating data for action units is an expensive and time-consuming task, which
leads to a scarce data situation. By combining multiple datasets from different
studies, the amount of training data for a machine learning algorithm can be
increased in order to create robust models for automated, multi-label action
unit detection. However, every study annotates different action units, leading
to a tremendous amount of missing labels in a combined database. In this work,
we examine this challenge and present our approach to create a combined
database and an algorithm capable of learning under the presence of missing
labels without inferring their values. Our approach shows competitive
performance compared to recent competitions in action unit detection.
