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arXiv · 2512.06275

FacePhys: State of the Heart Learning

Kegang Wang·Jiankai Tang·Yuntao Wang·Xin Liu·Yuxuan Fan·Jiatong Ji·Yuanchun Shi·Daniel McDuff
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Abstract

Vital sign measurement using cameras presents opportunities for comfortable, ubiquitous health monitoring. Remote photoplethysmography (rPPG), a foundational technology, enables cardiac measurement through minute changes in light reflected from the skin. However, practical deployment is limited by the computational constraints of performing analysis on front-end devices and the accuracy degradation of transmitting data through compressive channels that reduce signal quality. We propose a memory efficient rPPG algorithm - \emph{FacePhys} - built on temporal-spatial state space duality, which resolves the trilemma of model scalability, cross-dataset generalization, and real-time operation. Leveraging a transferable heart state, FacePhys captures subtle periodic variations across video frames while maintaining a minimal computational overhead, enabling training on extended video sequences and supporting low-latency inference. FacePhys establishes a new state-of-the-art, with a substantial 49\% reduction in error. Our solution enables real-time inference with a memory footprint of 3.6 MB and per-frame latency of 9.46 ms -- surpassing existing methods by 83\% to 99\%. These results translate into reliable real-time performance in practical deployments, and a live demo is available at https://www.facephys.com/.

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Kegang Wang, Jiankai Tang, Yuntao Wang, Xin Liu, Yuxuan Fan, Jiatong Ji, Yuanchun Shi, Daniel McDuff. 2025-12-06. FacePhys: State of the Heart Learning. https://arxiv.org/abs/2512.06275

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