无镜头相机的隐私保护人脸识别与验证

IF 5
Chris Henry;M. Salman Asif;Zhu Li
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引用次数: 0

摘要

如今,面部识别技术变得越来越普遍。面部识别系统依赖于大量的面部图像数据。这引发了严重的隐私问题,因为考虑到数据泄露或黑客攻击的持续风险,安全存储这些面部数据是具有挑战性的。本文提出了一种不损害用户隐私的隐私保护人脸识别与验证系统。它利用无镜头相机——平板相机捕捉到的传感器测量值。这些传感器测量结果在视觉上是不可理解的,保护了用户的隐私。我们的解决方案不需要了解相机传感器的点扩展函数,也不需要在任何阶段重建图像。为了在没有人脸图像信息的情况下进行人脸识别,我们提出了一种可以在不暴露人脸图像的情况下识别人脸的离散余弦变换(DCT)域传感器测量学习方案。我们通过在多个分辨率下计算传感器测量的DCT,然后将结果分成多个子带来计算频域表示。与直接用传感器测量训练得到的精度相比,使用这种DCT表示训练的网络得到了巨大的精度提升。此外,我们在提出的DCT表示中引入随机DCT系数位置的伪随机噪声作为密钥,进一步提高了系统的安全性。如果不知道相机参数和噪声位置,从DCT表示中恢复人脸图像几乎是不可能的。我们在一个真实的无镜头相机数据集——FlatCam Face数据集上对该系统进行了评估。实验结果表明,该系统具有较高的安全性,识别准确率达到93.97%,同时保持了较强的用户隐私性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Privacy-Preserving Face Recognition and Verification With Lensless Camera
Facial recognition technology is becoming increasingly ubiquitous nowadays. Facial recognition systems rely upon large amounts of facial image data. This raises serious privacy concerns since storing this facial data securely is challenging given the constant risk of data breaches or hacking. This paper proposes a privacy-preserving face recognition and verification system that works without compromising the user’s privacy. It utilizes sensor measurements captured by a lensless camera - FlatCam. These sensor measurements are visually unintelligible, preserving the user’s privacy. Our solution works without the knowledge of the camera sensor’s Point Spread Function and does not require image reconstruction at any stage. In order to perform face recognition without information on face images, we propose a Discrete Cosine Transform (DCT) domain sensor measurement learning scheme that can recognize faces without revealing face images. We compute a frequency domain representation by computing the DCT of the sensor measurement at multiple resolutions and then splitting the result into multiple subbands. The network trained using this DCT representation results in huge accuracy gains compared to the accuracy obtained after directly training with sensor measurement. In addition, we further enhance the security of the system by introducing pseudo-random noise at random DCT coefficient locations as a secret key in the proposed DCT representation. It is virtually impossible to recover the face images from the DCT representation without the knowledge of the camera parameters and the noise locations. We evaluated the proposed system on a real lensless camera dataset - the FlatCam Face dataset. Experimental results demonstrate the system is highly secure and can achieve a recognition accuracy of 93.97% while maintaining strong user privacy.
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CiteScore
10.90
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