Facial Image and Video Pain Intensity Estimation

Effective pain assessment is crucial in clinic practice, as it directly impacts treatment decisions and patients' outcomes. Regular pain evaluation relies on self-reported scales, which are often limited by subjectivity and variability. This study investigates advanced machine learning techniques, specifically 3DCNN, TSN, ConvLSTM and VideoMAE architecture, for automated pain expression intensity assessment from videos. Using videos capturing peak pain and pain-free states, we trained and evaluated these models on the Biovid Heat Pain Database, with distinct participant sets for each phase. VideoMAE model demonstrated promising classification performance, achieving an average accuracy, F1 score, recall, and precision of 0.71 in distinguishing between maximum pain and no pain from videos. Among the models tested, four classification architectures demonstrated better performance with regards to accuracy compared to previous studies, underscoring their potential advantage in clinical pain assessment applications. Further work is necessary to expand dataset volume and diversity to improve model generalization and minimize biases. Additionally, exploring emerging lightweight, real-time models is crucial to enable seamless integration into mobile and wearable systems.