论文标题
使用合成化头像来推进非接触式生命体征测量
Advancing Non-Contact Vital Sign Measurement using Synthetic Avatars
论文作者
论文摘要
非接触性生理测量有可能提供低成本，非侵入性健康监测。但是，机器视觉方法通常受到带注释的视频数据集的可用性和多样性的限制，导致对复杂现实生活条件的普遍性不佳。为了应对这些挑战，这项工作提出了使用合成的化身，这些化身显示面部血流变化，并允许在各种条件下系统地生成样品。我们的结果表明，对模拟和真实视频数据的培训都可以在具有挑战性的条件下导致绩效提高。我们在三个大型基准数据集上显示出最先进的性能，并提高了皮肤类型和运动的鲁棒性。
Non-contact physiological measurement has the potential to provide low-cost, non-invasive health monitoring. However, machine vision approaches are often limited by the availability and diversity of annotated video datasets resulting in poor generalization to complex real-life conditions. To address these challenges, this work proposes the use of synthetic avatars that display facial blood flow changes and allow for systematic generation of samples under a wide variety of conditions. Our results show that training on both simulated and real video data can lead to performance gains under challenging conditions. We show state-of-the-art performance on three large benchmark datasets and improved robustness to skin type and motion.
