GFT-GCN：基于谱扩散的隐私保护3D人脸网格识别
GFT-GCN: Privacy-Preserving 3D Face Mesh Recognition with Spectral Diffusion
AI总结：
GFT-GCN通过结合谱图学习与扩散机制，实现隐私保护的3D人脸识别，兼顾高准确性和防攻击能力。
摘要：
3D人脸识别通过捕捉面部几何结构提供了一种稳健的生物特征解决方案，能够抵御光照变化、姿态变化和欺骗攻击的影响。其强大的防伪能力使其适用于高安全应用，但保护存储的生物特征模板仍然至关重要。我们提出了GFT-GCN，一种结合谱图学习与扩散基模板保护的隐私保护3D人脸识别框架。我们的方法通过图傅里叶变换（GFT）和图卷积网络（GCN）从3D人脸网格中提取紧凑且具有判别性的谱特征。为了保护这些特征，我们引入了谱扩散机制，生成不可逆、可再生且不可链接的模板。轻量级的客户端-服务器架构确保原始生物特征数据从未离开客户端设备。在BU-3DFE和FaceScape数据集上的实验显示了高识别准确性和对重建攻击的强抵抗力。结果表明，GFT-GCN有效平衡了隐私和性能，为安全的3D人脸认证提供了实用解决方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
