GFT-GCN: Privacy-Preserving 3D Face Mesh Recognition with Spectral Diffusion

3D face recognition offers a promising biometric solution by capturing the geometric structure of facial surfaces, making it robust to lighting conditions, pose variations, and presentation attacks. Its strong resistance to spoofing makes it particularly attractive for deployment in high-security applications. However, as biometric systems store sensitive identity information, the protection of biometric templates becomes critical. In this paper, we present GFT-GCN, a privacy-preserving framework for 3D face recognition that addresses this challenge through a combination of spectral graph learning and diffusion-based template protection. Our method integrates the Graph Fourier Transform (GFT) and Graph Convolutional Networks (GCN) to extract discriminative and compact spectral features from 3D face meshes. To protect these features, we introduce a novel spectral diffusion mechanism that generates irreversible, renewable, and unlinkable templates. A lightweight client-server architecture ensures that raw biometric data never leaves the client device. We evaluate our system on the BU-3DFE and FaceScape datasets, demonstrating high recognition performance and strong resistance to reconstruction attacks. Our results show that GFT-GCN effectively balances privacy and accuracy, providing a practical solution for secure 3D face authentication.