  Automated emotion classification could aid those who struggle to recognize
emotion, including children with developmental behavioral conditions such as
autism. However, most computer vision emotion models are trained on adult
affect and therefore underperform on child faces. In this study, we designed a
strategy to gamify the collection and the labeling of child affect data in an
effort to boost the performance of automatic child emotion detection to a level
closer to what will be needed for translational digital healthcare. We
leveraged our therapeutic smartphone game, GuessWhat, which was designed in
large part for children with developmental and behavioral conditions, to gamify
the secure collection of video data of children expressing a variety of
emotions prompted by the game. Through a secure web interface gamifying the
human labeling effort, we gathered and labeled 2,155 videos, 39,968 emotion
frames, and 106,001 labels on all images. With this drastically expanded
pediatric emotion centric database (>30x larger than existing public pediatric
affect datasets), we trained a pediatric emotion classification convolutional
neural network (CNN) classifier of happy, sad, surprised, fearful, angry,
disgust, and neutral expressions in children. The classifier achieved 66.9%
balanced accuracy and 67.4% F1-score on the entirety of CAFE as well as 79.1%
balanced accuracy and 78.0% F1-score on CAFE Subset A, a subset containing at
least 60% human agreement on emotions labels. This performance is at least 10%
higher than all previously published classifiers, the best of which reached
56.% balanced accuracy even when combining "anger" and "disgust" into a single
class. This work validates that mobile games designed for pediatric therapies
can generate high volumes of domain-relevant datasets to train state of the art
classifiers to perform tasks highly relevant to precision health efforts.
