  Emotion recognition in children can help the early identification of, and
intervention on, psychological complications that arise in stressful situations
such as cancer treatment. Though deep learning models are increasingly being
adopted, data scarcity is often an issue in pediatric medicine, including for
facial emotion recognition in children. In this paper, we study the application
of data augmentation-based contrastive learning to overcome data scarcity in
facial emotion recognition for children. We explore the idea of ignoring
generational gaps, by adding abundantly available adult data to pediatric data,
to learn better representations. We investigate different ways by which adult
facial expression images can be used alongside those of children. In
particular, we propose to explicitly incorporate within each mini-batch adult
images as augmentations for children's. Out of $84$ combinations of learning
approaches and training set sizes, we find that supervised contrastive learning
with the proposed training scheme performs best, reaching a test accuracy that
typically surpasses the one of the second-best approach by 2% to 3%. Our
results indicate that adult data can be considered to be a meaningful
augmentation of pediatric data for the recognition of emotional facial
expression in children, and open up the possibility for other applications of
contrastive learning to improve pediatric care by complementing data of
children with that of adults.
