Orientation-conditioned Facial Texture Mapping for Video-based Facial Remote Photoplethysmography Estimation
arxiv(2024)
摘要
Camera-based remote photoplethysmography (rPPG) enables contactless
measurement of important physiological signals such as pulse rate (PR).
However, dynamic and unconstrained subject motion introduces significant
variability into the facial appearance in video, confounding the ability of
video-based methods to accurately extract the rPPG signal. In this study, we
leverage the 3D facial surface to construct a novel orientation-conditioned
facial texture video representation which improves the motion robustness of
existing video-based facial rPPG estimation methods. Our proposed method
achieves a significant 18.2
on MMPD over our baseline using the PhysNet model trained on PURE, highlighting
the efficacy and generalization benefits of our designed video representation.
We demonstrate significant performance improvements of up to 29.6
tested motion scenarios in cross-dataset testing on MMPD, even in the presence
of dynamic and unconstrained subject motion. Emphasizing the benefits the
benefits of disentangling motion through modeling the 3D facial surface for
motion robust facial rPPG estimation. We validate the efficacy of our design
decisions and the impact of different video processing steps through an
ablation study. Our findings illustrate the potential strengths of exploiting
the 3D facial surface as a general strategy for addressing dynamic and
unconstrained subject motion in videos. The code is available at
https://samcantrill.github.io/orientation-uv-rppg/.
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