Realistic Pulse Waveforms Estimation via Contrastive Learning in Remote Photoplethysmography

In recent years, video-based physiological parameter measurement has gained significant attention because of its noncontact and low-cost characteristics. While supervised learning-based methods have achieved state-of-the-art performance, less attention has been paid to the reliability of the reference labels. In most studies, finger pulse oximeters are commonly used to record photoplethysmography (PPG) signals, serving as ground truth rather than utilizing real facial PPG signals. However, the appropriateness of this substitution for reference labels has been scarcely investigated. In this article, we introduce a contact PPG acquisition system to acquire contact PPG signals of faces and fingers simultaneously and analyze their similarities and differences quantitatively. Experimental results reveal substantial disparities in morphology and phase between the PPG signal collected from the finger (Finger-PPG) and the PPG signal collected from the face (Facial-PPG). To eliminate the above differences, we propose a new framework: a contrastive learning-based method is used to generate PPG signals (Contrast-PPG) to enhance Finger-PPG as reference labels. A 3DCNN-based network is used as a PPG estimator. This framework combines supervised and unsupervised methods to further improve performance. Extensive experiments are conducted proving that the corrected PPG signals are closer to the real facial PPG signal, effectively improving the performance and generalization ability of the model.