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Shielding facial physiological information in video.

Kun Zheng1, Junjie Shen1, Guangmin Sun1

  • 1Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.

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This study introduces a novel method to protect personal physiological data in videos. By altering facial regions, it effectively shields sensitive information like heart rate, preventing privacy breaches without compromising video quality.

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Area of Science:

  • Computer Vision
  • Biomedical Signal Processing
  • Information Security

Background:

  • Non-contact physiological signal detection from videos, like remote photoplethysmography (rPPG), enables extraction of vital signs such as heart rate.
  • The widespread use of videos raises concerns about unintentional leakage of personal physiological information, posing privacy and security risks.

Purpose of the Study:

  • To propose and evaluate a novel method for shielding physiological information embedded within videos.
  • To ensure that the video quality is not significantly degraded during the information shielding process.

Main Methods:

  • Analysis of the remote photoplethysmography (rPPG) algorithm principles.
  • Selection of facial regions of interest (ROIs) with high signal-to-noise ratio for physiological data.
  • Application of signal-masking techniques, including periodic noise addition, blur filtering, and brightness adjustments, to ROIs.
  • Integration of processed ROIs back into video frames.

Main Results:

  • The proposed method demonstrated high interference efficiencies against established rPPG algorithms: 82.9% against Independent Component Analysis (ICA) and 84.6% against the Chrominance-based Method (CHROM).
  • Experimental validation on the VIPL-HR dataset confirmed the effectiveness of the proposed approach in obscuring physiological signals.
  • The method successfully shielded physiological information while maintaining significant video quality.

Conclusions:

  • The developed technique effectively protects sensitive physiological data captured in videos.
  • This method offers a viable solution for mitigating privacy risks associated with video-based physiological monitoring.
  • Further research can explore broader applications and optimizations for real-time implementation.