3WAUS: A novel three-way adaptive uncertainty-suppressing model for facial expression recognition

Abstract

Facial Expression Recognition (FER) remains a challenging task due to the uncertainty caused by the noisy labels, interrater disagreement, and the inter-class similarity of facial expressions. However, the majority of existing FER methods are deterministic, resulting in limited generalization performance in dealing with uncertainty. By adding a non-deterministic option, three-way decision (3WD) enhances the ability to handle uncertainty, which is widely used in different domains to reduce decision risk. Therefore, we introduce 3WD model into FER, and propose a novel three-way adaptive uncertainty-suppressing model (3WAUS). The model divides the sample space into three regions: high-importance, medium-importance, and low-importance, with different action strategies to effectively suppress the uncertainty in FER. Specifically, 3WAUS utilizes a weighted learning strategy to reinforce learning in the high-importance (low-uncertainty) region, thus acquiring high-quality sample features. For medium-importance samples, a dynamic adaptive re-labeling strategy is proposed to modify noisy labels, enabling the model to learn more accurate representations and avoid overfitting noise. Additionally, 3WAUS also attenuates the learning for low-importance (high-uncertainty) samples, effectively reducing the misguidance of noise on the model. Finally, extensive experiments on three public datasets and synthetic noisy datasets indicate that the proposed 3WAUS outperforms state-of-the-art methods in dealing with uncertainty.