  The task of predicting affective information in the wild such as seven basic
emotions or action units from human faces has gradually become more interesting
due to the accessibility and availability of massive annotated datasets. In
this study, we propose a method that utilizes the association between seven
basic emotions and twelve action units from the AffWild2 dataset. The method
based on the architecture of ResNet50 involves the multi-task learning
technique for the incomplete labels of the two tasks. By combining the
knowledge for two correlated tasks, both performances are improved by a large
margin compared to those with the model employing only one kind of label.
