基于面部视频的隐私保护式远程心率估计
Privacy-Preserving Remote Heart Rate Estimation from Facial Videos
AI总结：
针对面部视频rPPG估计中的隐私风险，提出提取低身份信息区域并结合像素混洗与模糊的数据扰动方法，在保持心率提取性能的同时显著降低人脸识别准确率。
摘要：
远程光电容积脉搏波描记法（rPPG）是从面部视频中估计PPG的过程。尽管该方法得益于非接触式交互，但它依赖面部视频，而这往往会引发重要的隐私担忧。近期研究表明，深度学习技术容易受到攻击，可能导致严重的数据泄露，这使得深度rPPG估计更加敏感。为解决这一问题，我们提出一种数据扰动方法：先提取面部中身份相关信息较少的特定区域，随后进行像素混洗和模糊处理。我们在两个rPPG数据集（PURE和UBFC）上的实验表明，该方法使面部识别算法的准确率降低超过60%，同时对rPPG提取的影响极小。我们还在三个人脸识别数据集（LFW、CALFW和AgeDB）上测试了该方法，其性能降低近50%。研究结果表明，该方法有潜力成为rPPG估计的有效隐私保护解决方案。
英文摘要：
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.
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