CodePhys: Robust Video-based Remote Physiological Measurement through Latent Codebook Querying
Shuyang Chu, Menghan Xia, Mengyao Yuan, Xin Liu, Tapio Seppanen,, Guoying Zhao, and Jingang Shi

TL;DR
CodePhys introduces a novel noise-robust approach for remote physiological measurement from facial videos by matching noisy signals with a ground-truth codebook, significantly improving accuracy under real-world interference.
Contribution
The paper presents CodePhys, a new method that models rPPG measurement as a code query task in a noise-free space, enhancing robustness against real-world video interference.
Findings
Outperforms state-of-the-art methods on four benchmark datasets.
Effective in both intra-dataset and cross-dataset evaluations.
Utilizes a spatial attention mechanism and distillation loss for improved signal fidelity.
Abstract
Remote photoplethysmography (rPPG) aims to measure non-contact physiological signals from facial videos, which has shown great potential in many applications. Most existing methods directly extract video-based rPPG features by designing neural networks for heart rate estimation. Although they can achieve acceptable results, the recovery of rPPG signal faces intractable challenges when interference from real-world scenarios takes place on facial video. Specifically, facial videos are inevitably affected by non-physiological factors (e.g., camera device noise, defocus, and motion blur), leading to the distortion of extracted rPPG signals. Recent rPPG extraction methods are easily affected by interference and degradation, resulting in noisy rPPG signals. In this paper, we propose a novel method named CodePhys, which innovatively treats rPPG measurement as a code query task in a noise-free…
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Taxonomy
TopicsECG Monitoring and Analysis · Non-Invasive Vital Sign Monitoring
MethodsSoftmax · Attention Is All You Need
