Personalized Pain Detection in Facial Video with Uncertainty Estimation

Pain is a personal, subjective experience, and the current gold standard to evaluate pain is the Visual Analog Scale (VAS), which is self-reported at the video level. One problem with the current automated pain detection systems is that the learned model doesn’t generalize well to unseen subjects. In this work, we propose to improve pain detection in facial videos using individual models and uncertainty estimation. For a new test video, we jointly consider which individual models generalize well generally, and which individual models are more similar/accurate to this test video, in order to choose the optimal combination of individual models and get the best performance on new test videos. We show on the UNBC-McMaster Shoulder Pain Dataset that our method significantly improves the previous state-of-the-art performance.

疼痛是一种个人的、主观的体验，目前评估疼痛的金标准是视觉模拟评分法（VAS），它是在视频层面自我报告的。当前自动疼痛检测系统的一个问题是，所学习的模型对未见过的受试者泛化能力不佳。在这项工作中，我们提议使用个体模型和不确定性估计来改进面部视频中的疼痛检测。对于一个新的测试视频，我们综合考虑哪些个体模型通常泛化能力好，以及哪些个体模型与这个测试视频更相似/更准确，以便选择个体模型的最佳组合，并在新的测试视频上获得最佳性能。我们在UNBC - 麦克马斯特肩部疼痛数据集上表明，我们的方法显著提高了先前的最先进性能。