Dynamic Facial Expression Recognition in Unconstrained Real-World Scenarios Leveraging Dempster-Shafer Evidence Theory

Dynamic facial expression recognition (DFER) has garnered significant attention due to its critical role in various applications, including human-computer interaction, emotion-aware systems, and mental health monitoring. Nevertheless, addressing the challenges of DFER in real-world scenarios remains a formidable task, primarily due to the severe class imbalance problem, leading to suboptimal model performance and poor recognition of minority class expressions. Recent studies in facial expression recognition (FER) for class imbalance predominantly focus on spatial features analysis, while the capacity to encode temporal features of spontaneous facial expressions remains limited. To tackle this issue, we introduce a novel dynamic facial expression recognition in real-world scenarios (RS-DFER) framework, which primarily comprises a spatiotemporal features combination (STC) module and a multi-classifier dynamic participation (MCDP) module. Our extensive experiments on two prevalent large-scale DFER datasets from real-world scenarios demonstrate that our proposed method outperforms existing state-of-the-art approaches, showcasing its efficacy and potential for practical applications.