Contactless Pulse Estimation Leveraging Pseudo Labels and Self-Supervision

Remote photoplethysmography (rPPG) is a promising research area involving non-invasive monitoring of vital signs using cameras. While several supervised methods have been proposed, recent research has focused on contrastive-based self-supervised methods. However, these methods often collapse to learning irrelevant periodicities when dealing with interferences such as head motions, facial dynamics, and video compression. To address this limitation, firstly, we enhance the current self-supervised learning by introducing more reliable and explicit contrastive constraints. Secondly, we propose an innovative learning strategy that seamlessly integrates self-supervised constraints with pseudo-supervisory signals derived from traditional unsupervised methods. This is followed by a co-rectification technique designed to mitigate the adverse effects of noisy pseudo-labels. Experimental results demonstrate the superiority of our methodology over representative models when applied to small, high-quality datasets such as PURE and UBFC-rPPG. Importantly, on large-scale challenging datasets such as VIPL-HR and V4V, our method, with zero annotation cost, not only significantly surpasses prevailing self-supervised techniques but also showcases remarkable alignment with state-of-the-art supervised methods.