Dual-Bridging With Adversarial Noise Generation for Domain Adaptive rPPG Estimation

Jingda Du, Siqi Liu, Bochao Zhang, Pong Chi Yuen

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

3 Citations (Scopus)

Abstract

The remote photoplethysmography (rPPG) technique can estimate pulse-related metrics (e.g. heart rate and respiratory rate) from facial videos and has a high potential for health monitoring. The latest deep rPPG methods can model in-distribution noise due to head motion, video compression, etc., and estimate high-quality rPPG signals under similar scenarios. However, deep rPPG models may not generalize well to the target test domain with unseen noise and distortions. In this paper, to improve the generalization ability of rPPG models, we propose a dual-bridging network to reduce the domain discrepancy by aligning intermediate domains and synthesizing the target noise in the source domain for better noise reduction. To comprehensively explore the target domain noise, we propose a novel adversarial noise generation in which the noise generator indirectly competes with the noise reducer. To further improve the robustness of the noise reducer, we propose hard noise pattern mining to encourage the generator to learn hard noise patterns contained in the target domain features. We evaluated the proposed method on three public datasets with different types of interferences. Under different cross-domain scenarios, the comprehensive results show the effectiveness of our method.
Original languageEnglish
Title of host publicationProceedings - 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023
PublisherIEEE
Pages10355-10364
Number of pages10
ISBN (Electronic)9798350301298
DOIs
Publication statusPublished - Jun 2023
Event36th IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023 - Vancouver, Canada
Duration: 18 Jun 202322 Jun 2023
https://cvpr2023.thecvf.com/virtual/2023/index.html
https://openaccess.thecvf.com/CVPR2023
https://cvpr2023.thecvf.com/virtual/2023/papers.html?filter=titles
https://ieeexplore.ieee.org/xpl/conhome/10203037/proceeding

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume2023-June
ISSN (Print)1063-6919

Conference

Conference36th IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023
Country/TerritoryCanada
CityVancouver
Period18/06/2322/06/23
Internet address

Scopus Subject Areas

  • Software
  • Computer Vision and Pattern Recognition

User-Defined Keywords

  • Biometrics

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