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<p>The revolutionary remote photoplethysmography (rPPG) technique has enabled intelligent devices to estimate physiological parameters with remarkable accuracy. However, the continuous and surreptitious recording of individuals by these devices and the collecting of sensitive health data without users’ knowledge or consent raise serious privacy concerns. Here we explore frugal methods for modifying facial videos to conceal physiological signals while maintaining image quality. Eleven lightweight modification methods, including blurring operations, additive noises, and time-averaging techniques, were evaluated using five different rPPG techniques across four activities: rest, talking, head rotation, and gym. These rPPG methods require minimal computational resources, enabling real-time implementation on low-compute devices. Our results indicate that the time-averaging sliding frame method achieved the greatest balance between preserving the information within the frame and inducing a heart rate error, with an average error of 22 beats per minute (bpm). Further, the facial region of interest was found to be the most effective and to offer the best trade-off between bpm errors and information loss.</p>

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Preserving privacy and video quality through remote physiological signal removal

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月7日

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  • Saksham Bhutani,
  • Mohamed Elgendi,
  • Carlo Menon

摘要

The revolutionary remote photoplethysmography (rPPG) technique has enabled intelligent devices to estimate physiological parameters with remarkable accuracy. However, the continuous and surreptitious recording of individuals by these devices and the collecting of sensitive health data without users’ knowledge or consent raise serious privacy concerns. Here we explore frugal methods for modifying facial videos to conceal physiological signals while maintaining image quality. Eleven lightweight modification methods, including blurring operations, additive noises, and time-averaging techniques, were evaluated using five different rPPG techniques across four activities: rest, talking, head rotation, and gym. These rPPG methods require minimal computational resources, enabling real-time implementation on low-compute devices. Our results indicate that the time-averaging sliding frame method achieved the greatest balance between preserving the information within the frame and inducing a heart rate error, with an average error of 22 beats per minute (bpm). Further, the facial region of interest was found to be the most effective and to offer the best trade-off between bpm errors and information loss.