Toward Class Imbalance in Facial Pain Recognition for Transportation Safety: An Enhanced ResNet–BiLSTM Framework

Pain is a complex experience involving both sensations and emotions, and its accurate assessment is essential in healthcare and intelligent transportation. Where diverse traffic environments can heighten driver discomfort, leading to fatigue, reduced focus, and increased accident risk. In recent years, extensive studies have demonstrated the potential of deep learning techniques for automatically predicting pain intensity by analyzing pain‐related facial expressions. However, two fundamental challenges remain in real‐world applications: the uncertainties arising from nonstandardized data collection and the class imbalance problem in pain assessment datasets. Addressing this issue is essential to improving recognition accuracy and robustness. This study proposes a novel pain level recognition framework based on a ResNet–BiLSTM architecture. First, the Squeeze‐and‐Excitation attention mechanism is integrated to enhance the model’s ability to capture basic pain‐related facial features, thereby increasing robustness against data uncertainties. Additionally, the I‐center loss function is introduced during model optimization, enhancing intraclass compactness and interclass separability to improve class‐imbalanced model learning. Experimental results on the BioVid Heat Pain Database demonstrate that this approach outperforms state‐of‐the‐art baselines, achieving 99.67% accuracy. Furthermore, ablation studies validate the effectiveness of the proposed components in our framework.