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

Since even experienced annotators Emotion recognition and understanding is a vital component may disagree on these labels, multiple annotations per image in human-machine interaction. Dimensional models of affect are required, which further increases the cost and complexity such as those using valence and arousal have advantages over of the task. Yet there are no guarantees that the full range of traditional categorical ones due to the complexity of emotional possible expressions and intensities will be covered, resulting states in humans. However, dimensional emotion annotations in imbalanced datasets, with only few images with'interesting' are difficult and expensive to collect, therefore they affective content. Consequently, large, balanced emotion are still limited in the affective computing community. To address datasets, with high-quality annotations, covering a wide range these issues, we propose a method to generate synthetic of expression variations and expression intensities of many images from existing categorical emotion datasets using face different subjects, are in short supply.