In smart classroom environments, accurately recognizing students’ facial expressions is crucial for teachers to efficiently assess students’ learning states, timely adjust teaching strategies, and enhance teaching quality and effectiveness. In this paper, we propose a student facial expression recognition model based on multi-scale and deep fine-grained feature attention enhancement (SFER-MDFAE) to address the issues of inaccurate facial feature extraction and poor robustness of facial expression recognition in smart classroom scenarios. Firstly, we construct a novel multi-scale dual-pooling feature aggregation module to capture and fuse facial information at different scales, thereby obtaining a comprehensive representation of key facial features; secondly, we design a key region-oriented attention mechanism to focus more on the nuances of facial expressions, further enhancing the representation of multi-scale deep fine-grained feature; finally, the fusion of multi-scale and deep fine-grained attention-enhanced features is used to obtain richer and more accurate facial key information and realize accurate facial expression recognition. The experimental results demonstrate that the proposed SFER-MDFAE outperforms the existing state-of-the-art methods, achieving an accuracy of 76.18% on FER2013, 92.75% on FERPlus, 92.93% on RAF-DB, 67.86% on AffectNet, and 93.74% on the real smart classroom facial expression dataset (SCFED). These results validate the effectiveness of the proposed method.

Accurate recognition of student emotions in smart classrooms is vital for understanding learning states. Visible light-based facial expression recognition is often affected by illumination changes, making thermal infrared imaging a promising alternative due to its robust temperature distribution symmetry. This paper proposes CTIFERK, a thermal infrared facial expression recognition model integrating Kolmogorov–Arnold Networks (KANs). By incorporating multiple KAN layers, CTIFERK enhances feature extraction and fitting capabilities. It also balances pooling layer information from the MobileViT backbone to preserve symmetrical facial features, improving recognition accuracy. Experiments on the Tufts Face Database, the IRIS Database, and the self-constructed GUET thermalface dataset show that CTIFERK achieves accuracies of 81.82%, 82.19%, and 65.22%, respectively, outperforming baseline models. These results validate CTIFERK’s effectiveness and superiority for thermal infrared expression recognition in smart classrooms, enabling reliable emotion monitoring.