Privacy-Preserving Remote Heart Rate Estimation from Facial Videos
This addresses privacy concerns for users of contactless health monitoring systems, offering a practical solution to protect identity while maintaining functionality, though it is incremental as it builds on existing perturbation techniques.
The paper tackles the privacy risk in remote heart rate estimation from facial videos by proposing a data perturbation method that extracts less identity-revealing face areas and applies pixel shuffling and blurring, reducing facial recognition accuracy by over 60% on rPPG datasets and nearly 50% on facial recognition datasets with minimal impact on heart rate extraction.
Remote Photoplethysmography (rPPG) is the process of estimating PPG from facial videos. While this approach benefits from contactless interaction, it is reliant on videos of faces, which often constitutes an important privacy concern. Recent research has revealed that deep learning techniques are vulnerable to attacks, which can result in significant data breaches making deep rPPG estimation even more sensitive. To address this issue, we propose a data perturbation method that involves extraction of certain areas of the face with less identity-related information, followed by pixel shuffling and blurring. Our experiments on two rPPG datasets (PURE and UBFC) show that our approach reduces the accuracy of facial recognition algorithms by over 60%, with minimal impact on rPPG extraction. We also test our method on three facial recognition datasets (LFW, CALFW, and AgeDB), where our approach reduced performance by nearly 50%. Our findings demonstrate the potential of our approach as an effective privacy-preserving solution for rPPG estimation.