  Understanding emotions and expressions is a task of interest across multiple
disciplines, especially for improving user experiences. Contrary to the common
perception, it has been shown that emotions are not discrete entities but
instead exist along a continuum. People understand discrete emotions
differently due to a variety of factors, including cultural background,
individual experiences, and cognitive biases. Therefore, most approaches to
expression understanding, particularly those relying on discrete categories,
are inherently biased. In this paper, we present a comparative in-depth
analysis of two common datasets (AffectNet and EMOTIC) equipped with the
components of the circumplex model of affect. Further, we propose a model for
the prediction of facial expressions tailored for lightweight applications.
Using a small-scaled MaxViT-based model architecture, we evaluate the impact of
discrete expression category labels in training with the continuous valence and
arousal labels. We show that considering valence and arousal in addition to
discrete category labels helps to significantly improve expression inference.
The proposed model outperforms the current state-of-the-art models on
AffectNet, establishing it as the best-performing model for inferring valence
and arousal achieving a 7% lower RMSE. Training scripts and trained weights to
reproduce our results can be found here:
https://github.com/wagner-niklas/CAGE_expression_inference.
