ML Anthology

Deep Learning-Based Image Enhancement for Robust Remote Photoplethysmography in Various Illumination Scenarios

Shutao Chen, Sui Kei Ho, Jing Wei Chin, Kin Ho Luo, Tsz Tai Chan, Richard Hau Yue So, Kwan Long Wong
CVPRW 2023 pp. 6077-6085
doi:10.1109/CVPRW59228.2023.00647 /cvprw/2023/chen2023cvprw-deep/

Abstract

Remote photoplethysmography (rPPG) is a non-invasive and convenient approach for measuring human vital signs using a camera. However, accurate measurement can be challenging due to the different illumination of the surrounding environment. In this study, we present a deep learning-based image enhancement model (IEM) inspired by the Retinex theory to improve the robustness of rPPG signal extraction and heart rate (HR) estimation in different lighting conditions. We fine-tuned the IEM with a time-shifted negative Pearson correlation between the PPG signal ground truth and the predicted rPPG signal from a pre-trained 3D CNN (PhysNet). We evaluated our method using a publicly available dataset (BH-rPPG) of various lighting scenarios and our own private dataset. Our results demonstrate that our proposed model is generalizable and significantly improves rPPG extraction and HR estimation accuracies across a range of illumination intensities.

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Cite

Text

Chen et al. "Deep Learning-Based Image Enhancement for Robust Remote Photoplethysmography in Various Illumination Scenarios." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2023. doi:10.1109/CVPRW59228.2023.00647

Markdown

[Chen et al. "Deep Learning-Based Image Enhancement for Robust Remote Photoplethysmography in Various Illumination Scenarios." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2023.](https://mlanthology.org/cvprw/2023/chen2023cvprw-deep/) doi:10.1109/CVPRW59228.2023.00647

BibTeX

@inproceedings{chen2023cvprw-deep,
  title     = {{Deep Learning-Based Image Enhancement for Robust Remote Photoplethysmography in Various Illumination Scenarios}},
  author    = {Chen, Shutao and Ho, Sui Kei and Chin, Jing Wei and Luo, Kin Ho and Chan, Tsz Tai and So, Richard Hau Yue and Wong, Kwan Long},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2023},
  pages     = {6077-6085},
  doi       = {10.1109/CVPRW59228.2023.00647},
  url       = {https://mlanthology.org/cvprw/2023/chen2023cvprw-deep/}
}