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

• URL: http://arxiv.org/abs/2103.02854v1
• Date: Thu, 4 Mar 2021 06:33:06 GMT
• Title: Morphset:Augmenting categorical emotion datasets with dimensional affect labels using face morphing
• Authors: Vassilios Vonikakis, Dexter Neo, Stefan Winkler
• Abstract summary: We propose a method to generate syntheticimages from existing categorical emotion datasets using facemorphing. This method achieves augmentation factors of at least 20x or more.
• Score: 3.8558530661279224
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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