Recent studies demonstrated that the average heart rate (HR) can be measured
from facial videos based on non-contact remote photoplethysmography (rPPG).
However for many medical applications (e.g., atrial fibrillation (AF)
detection) knowing only the average HR is not sufficient, and measuring precise
rPPG signals from face for heart rate variability (HRV) analysis is needed.
Here we propose an rPPG measurement method, which is the first work to use deep
spatio-temporal networks for reconstructing precise rPPG signals from raw
facial videos. With the constraint of trend-consistency with ground truth pulse
curves, our method is able to recover rPPG signals with accurate pulse peaks.
Comprehensive experiments are conducted on two benchmark datasets, and results
demonstrate that our method can achieve superior performance on both HR and HRV
levels comparing to the state-of-the-art methods. We also achieve promising
results of using reconstructed rPPG signals for AF detection and emotion
recognition.

通过使用深度时空网络重建精确的远程光学心电图信号，我们提出了一种 rPPG 测量方法，能够从面部视频中测量心率和心率变异性，并在心房颤动检测和情感识别方面取得了有前途的成果。