Morphset:Augmenting categorical emotion datasets with dimensional affect labels using face morphing
This addresses the problem of expensive and limited dimensional emotion data for the affective computing community, though it is incremental as it builds on existing categorical datasets.
The paper tackles the scarcity of dimensional emotion annotations by proposing a method to generate synthetic images with dimensional labels from existing categorical datasets using face morphing, achieving augmentation factors of at least 20x.
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 not as prevalent 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 as well as dimensional labels in the circumplex space with full control over the resulting sample distribution, while achieving augmentation factors of at least 20x or more.