Abstract
Face recognition is central to many authentication, security, and personalized applications. Yet, it suffers from significant privacy risks, particularly arising from unauthorized access to sensitive biometric data. This paper introduces CryptoFace, the first end-to-end encrypted face recognition system with fully homomorphic encryption (FHE). It enables secure processing of facial data across all stages of a face-recognition process--feature extraction, storage, and matching--without exposing raw images or features. We introduce a mixture of shallow patch convolutional networks to support higher-dimensional tensors via patch-based processing while reducing the multiplicative depth and, thus, inference latency. Parallel FHE evaluation of these networks ensures near-resolution-independent latency. On standard face recognition benchmarks, CryptoFace significantly accelerates inference and increases verification accuracy compared to the state-of-the-art FHE neural networks adapted for face recognition. CryptoFace will facilitate secure face recognition systems requiring robust and provable security. The code is available at this https URL.
Abstract (translated)
面部识别是许多认证、安全和个人化应用的核心，但它面临着严重的隐私风险，尤其是由于未经授权访问敏感的生物特征数据。本文介绍了CryptoFace，这是第一个使用全同态加密（FHE）实现端到端加密的脸部识别系统。它能够在脸部识别过程的所有阶段——从特征提取、存储到匹配——对面部数据进行安全处理，而不泄露原始图像或特征。 我们引入了一种浅层补丁卷积网络的混合方式，通过基于补丁的处理来支持更高维度的数据张量，并减少了乘法深度，从而降低了推理延迟。并行化FHE评估这些网络确保了接近分辨率无关性的延迟时间。在标准面部识别基准测试中，CryptoFace与目前适应脸部识别的最佳FHE神经网络相比，在加速推理和提高验证准确性方面取得了显著进步。 CryptoFace将有助于建立安全的面部识别系统，要求具备强大的和可证明的安全性保障。相关代码可以在提供的链接中获取。
URL
https://arxiv.org/abs/2509.00332
