用频谱扩散技术保护3D人脸特征，兼顾隐私与识别精度
GFT-GCN: Privacy-Preserving 3D Face Mesh Recognition with Spectral Diffusion
- 结合图傅里叶变换与图卷积网络提取紧凑频谱特征
- 频谱扩散使特征不可逆、可重置且无法关联，抗重建攻击
- 客户端本地处理，原始数据不出设备，适合高安全场景
3D人脸识别通过捕捉面部几何结构，对光照变化、姿态差异和呈现攻击具有强鲁棒性，适用于高安全场景，但生物特征模板的存储安全至关重要。本文提出GFT-GCN框架，融合频谱图学习与基于扩散的模板保护机制。利用图傅里叶变换（GFT）与图卷积网络（GCN）从3D人脸网格中提取紧凑且具区分性的频谱特征。为保护这些特征，引入频谱扩散机制，生成不可逆、可重置且无法关联的模板。采用轻量级客户端-服务器架构，确保原始生物特征数据始终留在客户端设备。在BU-3DFE和FaceScape数据集上的实验表明，GFT-GCN兼具高识别准确率与强抗重建攻击能力，有效平衡隐私与性能，为安全3D人脸认证提供实用方案。
原文摘要 · Abstract (English)
3D face recognition offers a robust biometric solution by capturing facial geometry, providing resilience to variations in illumination, pose changes, and presentation attacks. Its strong spoof resistance makes it suitable for high-security applications, but protecting stored biometric templates remains critical. We present GFT-GCN, a privacy-preserving 3D face recognition framework that combines spectral graph learning with diffusion-based template protection. Our approach integrates the Graph Fourier Transform (GFT) and Graph Convolutional Networks (GCN) to extract compact, discriminative spectral features from 3D face meshes. To secure these features, we introduce a spectral diffusion mechanism that produces irreversible, renewable, and unlinkable templates. A lightweight client-server architecture ensures that raw biometric data never leaves the client device. Experiments on the BU-3DFE and FaceScape datasets demonstrate high recognition accuracy and strong resistance to reconstruction attacks. Results show that GFT-GCN effectively balances privacy and performance, offering a practical solution for secure 3D face authentication.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。