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The task of predicting affective information in the wild such as seven basicemotions or action units from human faces has gradually become more interestingdue to the accessibility and availability of massive annotated datasets. Inthis study, we propose a method that utilizes the association between sevenbasic emotions and twelve action units from the AffWild2 dataset. The methodbased on the architecture of ResNet50 involves the multi-task learningtechnique for the incomplete labels of the two tasks. By combining theknowledge for two correlated tasks, both performances are improved by a largemargin compared to those with the model employing only one kind of label.

Emotion Recognition with Incomplete Labels Using Modified Multi-task Learning Technique