CoDPN: An Unsupervised Collaborative Dual-path Network For Contactless Remote Physiological Measurement

Remote photoplethysmography (rPPG) provides a convenient solution for contactless physiological measurement, and is a promising technique for daily health monitoring and clinical application. However, traditional supervised methods rely heavily on labeled data, incurring substantial annotation costs. Current unsupervised contrastive learning methods encounter challenges under challenging conditions, including variations in illumination and motion of the head or limbs. In this paper, we propose a novel unsupervised end-to-end framework, called CoDPN for physiological measurement. CoDPN employs a dual-path architecture consisting of the Short-term Information Extraction Path (SIEP) and Long-term Information Extraction Path (LIEP), which capture short-term contextual relevance and long-term periodic dependence of rPPG signals, respectively. Next, we propose a collaborative learning strategy to integrate the latent features from both the SIEP and LIEP, facilitating the exchange of complementary information. Furthermore, our unsupervised learning strategy leverages rPPG features in both the frequency and time domains, guiding the CoDPN to extract rPPG signals consistent with physiological information while reducing dependence on labeled datasets. We conduct extensive experiments on three benchmark datasets (UBFC-rPPG, PURE, and UBFC-phys) to implement physiological measurement, including heart rate (HR), heart rate variability (HRV) and respiratory frequency (RF). The experimental results demonstrate that our CoDPN outperforms other state-of-the-art methods under complex conditions.