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

Contrast-Phys+: Unsupervised and Weakly-supervised Video-based Remote Physiological Measurement via Spatiotemporal Contrast