MSDM: A Lightweight Multi-Scale Dynamic Mamba for Dynamic Facial Expression Recognition in Smart Classrooms

In smart classroom environments, dynamic facial expression recognition (DFER) is crucial for enhancing teaching quality and improving students' learning experiences. However, existing DFER models face significant challenges in computational efficiency and temporal modeling, which limit their practical application in resource-constrained settings. To address these issues, this paper proposes a novel lightweight DFER framework called Multi-Scale Dynamic Mamba (MSDM). The MSDM model combines a Multi-Scale Attention Fusion Module (MSAFM) to effectively integrate global and local facial features and a Dynamic Temporal Focus (DTF) mechanism to enhance the modeling of long-term facial expression dynamics. These components work together to highlight key facial muscle movements while reducing background interference. Additionally, we introduce Dual-Resolution Bidirectional Mamba (DR Bi-Mamba) blocks that process high- and low-resolution facial images in parallel for coarse-to-fine feature extraction. This bio-inspired strategy enhances robustness by effectively integrating global context and local details. To better align with the practical requirements of smart classroom scenarios, we have developed a dedicated classroom dataset, HM-Class, which addresses the mismatch between existing emotion categories and the high-frequency emotional states observed in educational contexts. Extensive experiments on seven in-the-wild datasets—four DFER datasets, two static facial expression recognition (SFER) datasets, and the HM-Class dataset—show that MSDM outperforms state-of-the-art methods with fewer parameters and lower computational costs. This study offers an efficient solution for affective computing in resource-constrained classroom environments and advances the practical application of DFER technology in educational settings.