Advances in Facial Pain Detection Research

This paper reviews the current state of the art and advances in research methods in the field of facial expression pain recognition, focusing on commonly used pain expression databases, pain rating scales, and facial pain detection methods. The paper first details two pain rating methods: self-rated and observer-rated, as well as facial expression detection datasets such as the UNBC-McGill Pain Archive and the BioVid Hot Pain Database. Subsequently, the applications of traditional machine learning methods and deep learning methods for facial pain detection are summarized, including geometric feature-based methods, epigenetic feature based methods, model-based methods, convolutional neural networks (cnn), recurrent neural networks (rnn) with long and short-term memory networks, and Transformer models. Finally, the article points out the current difficulties faced and future research directions, emphasizes the importance of building a large-scale database of pain expressions, and suggests that the use of deep learning to process these data will be a future trend that promises to significantly improve the accuracy and speed of pain recognition.