Paper
28 March 2024 Deep learning-based robust heart rate estimation using remote photoplethysmography under different illuminations
Zinan Huang, Chang-Hong Fu, Li Zhang, Hong Hong
Author Affiliations +
Proceedings Volume 13091, Fifteenth International Conference on Signal Processing Systems (ICSPS 2023); 130910N (2024) https://doi.org/10.1117/12.3022909
Event: Fifteenth International Conference on Signal Processing Systems (ICSPS 2023), 2023, Xi’an, China
Abstract
Remote photoplethysmography (rPPG) monitors heart rate (HR) without requiring physical contact, which has applications. However, accurate measurement can be challenging due to different illuminations of the surrounding environment, and the impact of different illuminations on deep learning-based methods is more severe than that of traditional methods. In this study, to improve the robustness of the model to extract rPPG signals and estimate HR under different illuminations, we proposed a method called TNDR (temporal normalization and DC removal) to reduce lighting information in facial video and tested it using 3D CNN network (PhysNet). We evaluated our proposed method using four publicly available datasets (PURE, UBFC-rPPG, UBFC-PHYS, LGI-PPGI) and a proposed dataset containing three illuminations. The results show that the proposed method can significantly improve the accuracy of rPPG extraction and HR estimation under different illuminations.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Zinan Huang, Chang-Hong Fu, Li Zhang, and Hong Hong "Deep learning-based robust heart rate estimation using remote photoplethysmography under different illuminations", Proc. SPIE 13091, Fifteenth International Conference on Signal Processing Systems (ICSPS 2023), 130910N (28 March 2024); https://doi.org/10.1117/12.3022909
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KEYWORDS
Light sources and illumination

Video

RGB color model

Education and training

Heart

Data modeling

Skin

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