CAGE: Circumplex Affect Guided Expression Inference

Niklas Wagner, Felix Mätzler, Samed R. Vossberg, Helen Schneider, Svetlana Pavlitska, J. Marius Zöllner; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 4683-4692

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.