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Photoreceptors and Visual Pathways01:22

Photoreceptors and Visual Pathways

At the molecular level, visual signals trigger transformations in photopigment molecules, resulting in changes in the photoreceptor cell's membrane potential. The photon's energy level is denoted by its wavelength, with each specific wavelength of visible light associated with a distinct color. The spectral range of visible light, classified as electromagnetic radiation, spans from 380 to 720 nm. Electromagnetic radiation wavelengths exceeding 720 nm fall under the infrared category, whereas...

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rePPG: Relighting Photoplethysmography Signal to Video.

Seunghyun Kim1, Yeongje Park2, Byeongseon An2

  • 1Department of AI Bigdata, Daejin University, 1007 Hoguk-ro, Pocheon-si 11159, Republic of Korea.

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Summary

This study introduces rePPG, a novel framework for privacy-preserving remote photoplethysmography (rPPG). It enables the insertion of target physiological signals into facial videos without compromising identity or visual quality.

Keywords:
biometric securitygenerative video modelingphysiological signal editingprivacy preservationremote photoplethysmography (rPPG)

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Area of Science:

  • Biomedical Engineering
  • Computer Vision
  • Signal Processing

Background:

  • Remote photoplethysmography (rPPG) enables contactless health monitoring via facial video analysis.
  • Facial videos contain sensitive biometric data, raising significant privacy concerns.
  • Current privacy methods degrade video quality, hindering practical rPPG applications.

Purpose of the Study:

  • To develop a privacy-preserving framework for rPPG that preserves facial appearance.
  • To enable the insertion of a desired rPPG signal into facial videos.
  • To ensure the reliable recovery of the injected rPPG signal.

Main Methods:

  • The rePPG framework disentangles facial appearance from physiological signals.
  • Skin segmentation localizes modifications to relevant facial regions.
  • A cycle-consistency mechanism ensures accurate rPPG signal recovery.

Main Results:

  • rePPG successfully embeds target photoplethysmography (PPG) signals into videos.
  • Achieved 1.10 BPM MAE and 95.00% PTE6 on the PURE dataset.
  • Preserved visual quality with PSNR of 24.61 dB and SSIM of 0.638.

Conclusions:

  • rePPG offers a viable solution for privacy-preserving rPPG by rewriting physiological signals.
  • The method maintains facial identity and visual integrity.
  • Evaluations confirm the accuracy of the rewritten rPPG signals.