TITLE: Morphset:Augmenting categorical emotion datasets with dimensional affect labels using face morphing 

ABSTRACT: 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.
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.
Morphset:Augmenting categorical emotion datasets with dimensional affect labels using face morphing
http://arxiv.org/abs/2103.02854v2