DIRE: Enhancing Facial Expression Recognition Through Domain-Invariant Representation Learning for Robust Generalization

In this paper, we propose DIRE (Domain-Invariant Representation Learning for Expression), a novel approach to enhance the generalizability of facial expression recognition (FER) models in unseen domains. Traditional FER models often struggle with distribution shifts between training and test datasets, leading to significant performance drops. Based on the concept of Single-Source Domain Generalization, we introduce a novel domain augmentation technique that applies pixel-level and feature-level perturbations to domain-variant regions while preserving semantic consistency. Additionally, we incorporate semantic alignment regularization and domain information minimization loss so that domain-invariant features effectively represent facial expressions. Extensive experiments on multiple FER datasets demonstrate that our method significantly improves generalization across diverse target domains, even when trained on a single source domain. The proposed DIRE approach offers a robust solution to real-world FER tasks, where unseen domain generalizability is crucial.