Video-based remote physiological measurement utilizes facial videos to
measure the blood volume change signal, which is also called remote
photoplethysmography (rPPG). Supervised methods for rPPG measurements have been
shown to achieve good performance. However, the drawback of these methods is
that they require facial videos with ground truth (GT) physiological signals,
which are often costly and difficult to obtain. In this paper, we propose
Contrast-Phys+, a method that can be trained in both unsupervised and
weakly-supervised settings. We employ a 3DCNN model to generate multiple
spatiotemporal rPPG signals and incorporate prior knowledge of rPPG into a
contrastive loss function. We further incorporate the GT signals into
contrastive learning to adapt to partial or misaligned labels. The contrastive
loss encourages rPPG/GT signals from the same video to be grouped together,
while pushing those from different videos apart. We evaluate our methods on
five publicly available datasets that include both RGB and Near-infrared
videos. Contrast-Phys+ outperforms the state-of-the-art supervised methods,
even when using partially available or misaligned GT signals, or no labels at
all. Additionally, we highlight the advantages of our methods in terms of
computational efficiency, noise robustness, and generalization.

视频远程生理测量是利用面部视频测量血容量变化信号，也称为远程光电容抗 (PRP)。本文提出了一种名为 Contrast-Phys + 的方法，可以在无监督和弱监督设置下进行训练。我们使用 3D 卷积神经网络模型来生成多个时空 PRP 信号，并将 PRP 的先验知识纳入对比损失函数中。我们进一步将 GT 信号纳入对比学习，以适应偏向性或错位标签。对比损失鼓励来自同一视频的 PRP/GT 信号进行分组，并将来自不同视频的信号分开。我们在包括 RGB 和近红外视频的五个公开数据集上评估我们的方法。即使在部分可用或错位的 GT 信号或没有标签的情况下，Contrast-Phys + 也优于最先进的有监督方法。此外，我们还强调了我们的方法在计算效率、噪声鲁棒性和泛化性方面的优势。