FPE-Net: Face Privacy-Enhancing Method Using Biometric Encryption

Donghua Jiang, Jiangqun Ni, Qingliang Liu, Jawad Ahmad, Wadii Boulila

Abstract: With the increasing reliance on the biometric-based authentication systems, such as face recognition, in applications within the IoT and edge networks, guaranteeing proper service functionality while safeguarding individual biometric privacy has become a critical concern. However, most existing face privacy protection approaches mainly focus on preserving the machine-recognizable identity information, inadvertently compromising individual privacy. To tackle this challenge, a novel Face Privacy-Enhancing Network (FPE-Net) is proposed, which consists of two primary stages: biometric encryption and face reconstruction. Specifically, a linear encryption module is designed in the first stage for obfuscating the original identity information, which is later integrated into the depth features of the target face via an identity injector. Notably, the identity encryption process operates independently of the deep generative network, enabling greater flexibility and efficiency for key configuration. Then in the second stage, a face decoder is utilized to synthesize the photo-realistic face. Moreover, such face not only prevents cross-matching with biometric databases but also preserves recognition utility, owing to the linear encryption mechanism and loss design. Extensive quantitative and qualitative experimental results demonstrate the feasibility of FPE-Net model, which outperforms existing state-of-the-art approaches in terms of privacy protection.

External IDs:dblp:conf/ijcnn/JiangNLAB25