  Balancing methods for single-label data cannot be applied to multi-label
problems as they would also resample the samples with high occurrences. We
propose to reformulate this problem as an optimization problem in order to
balance multi-label data. We apply this balancing algorithm to training
datasets for detecting isolated facial movements, so-called Action Units.
Several Action Units can describe combined emotions or physical states such as
pain. As datasets in this area are limited and mostly imbalanced, we show how
optimized balancing and then augmentation can improve Action Unit detection. At
the IEEE Conference on Face and Gesture Recognition 2020, we ranked third in
the Affective Behavior Analysis in-the-wild (ABAW) challenge for the Action
Unit detection task.
