Video-based remote physiological measurement utilizes face videos to measure
the blood volume change signal, which is also called remote
photoplethysmography (rPPG). Supervised methods for rPPG measurements achieve
state-of-the-art performance. However, supervised rPPG methods require face
videos and ground truth physiological signals for model training. In this
paper, we propose an unsupervised rPPG measurement method that does not require
ground truth signals for training. We use a 3DCNN model to generate multiple
rPPG signals from each video in different spatiotemporal locations and train
the model with a contrastive loss where rPPG signals from the same video are
pulled together while those from different videos are pushed away. We test on
five public datasets, including RGB videos and NIR videos. The results show
that our method outperforms the previous unsupervised baseline and achieves
accuracies very close to the current best supervised rPPG methods on all five
datasets. Furthermore, we also demonstrate that our approach can run at a much
faster speed and is more robust to noises than the previous unsupervised
baseline. Our code is available at
this https URL

本文提出了一种无监督的远程光学脉搏测量方法，利用 3D 卷积神经网络模型以及对比损失训练模型，可以在不需要参照信号的情况下提高精度，并可以更快地运行且更耐噪音。