Accurate pain assessment is crucial in healthcare for effective diagnosis and treatment; however, traditional methods relying on self-reporting are inadequate for populations unable to communicate their pain. Cutting-edge AI is promising for supporting clinicians in pain recognition using facial video data. In this paper, we enhance pain recognition by employing facial video analysis within a Transformer-based deep learning model. By combining a powerful Masked Autoencoder with a Transformers-based classifier, our model effectively captures pain level indicators through both expressions and micro-expressions. We conducted our experiment on the AI4Pain dataset, which produced promising results that pave the way for innovative healthcare solutions that are both comprehensive and objective.
@article{arxiv.2409.05088,
title = {Transformer with Leveraged Masked Autoencoder for video-based Pain Assessment},
author = {Minh-Duc Nguyen and Hyung-Jeong Yang and Soo-Hyung Kim and Ji-Eun Shin and Seung-Won Kim},
journal= {arXiv preprint arXiv:2409.05088},
year = {2024}
}