Morphset: Augmenting Categorical Emotion Datasets With Dimensional Affect Labels Using Face Morphing

Emotion recognition and understanding is a vital component in human-machine interaction. Dimensional models of affect such as those using valence and arousal have advantages over traditional categorical ones due to the complexity of emotional states in humans. However, dimensional emotion annotations are difficult and expensive to collect, therefore they are still limited in the affective computing community. To address these issues, we propose a method to generate synthetic images from existing categorical emotion datasets using face morphing, with full control over the resulting sample distribution as well as dimensional labels in the circumplex space, while achieving augmentation factors of at least 20x or more.