  Emotion recognition and understanding is a vital componentin human-machine
interaction. Dimensional models of affectsuch as those using valence and
arousal have advantages overtraditional categorical ones due to the complexity
of emo-tional states in humans. However, dimensional emotion an-notations are
difficult and expensive to collect, therefore theyare still limited in the
affective computing community. To ad-dress these issues, we propose a method to
generate syntheticimages from existing categorical emotion datasets using
facemorphing, with full control over the resulting sample distri-bution as well
as dimensional labels in the circumplex space,while achieving augmentation
factors of at least 20x or more.
