ZeroFace: Unified Privacy-Preserving Framework for Secure Face Verification

Face recognition (FR) is widely used in authentication and surveillance applications due to its high accuracy for identity verification and recognition. However, there are potential privacy risks existing within FR systems at different stages, particularly when sensitive biometric data can be accessed without authorization. Existing countermeasures focus on protecting users' privacy by concealing identity information that underlies the facial embedding. However, recent generative privacy attacks are able to reconstruct high-quality and identity-preserving face images from facial embeddings. In this work, we propose a secure and efficient framework, ZeroFace, for embedding protection. Firstly, a compression network is proposed to reduce the embedding dimensionality by mapping it into low-dimensional representations, while maintaining nearly lossless recognition accuracy via learning the multi-scale identity information. Furthermore, identity-specific transformation is proposed to further obfuscate the compressed embedding. Lastly, a secure matching protocol is designed to enhance embedding security by secretly computing cosine distance without sharing the user's compressed embedding with the database holder. Extensive experiments show ZeroFace largely maintains recognition accuracy and enhances the embedding robustness compared to the state-of-the-art face template protection algorithms.