Private Face Recognition Training Dataset Publication via Identity-Decoupled and Geometry-Preserving Face Distillation

2026-07-30

AI Summary

This work addresses the critical privacy risks associated with releasing face training data, where existing methods struggle to disentangle identity information while preserving the class structure essential for recognition. To overcome this challenge, the authors propose a novel identity-disentangled and geometry-preserving face distillation framework that explicitly separates source identity semantics from proxy identity geometry. By enforcing orthogonal geometric preservation and aligning relational topologies, the method effectively eliminates linkability to original identities while retaining the hyperspherical proxy structure necessary for face recognition. Experimental results demonstrate that the proposed approach achieves a 3.94% improvement in TAR@FAR=1e-3 on the IJB-C surveillance benchmark, significantly outperforming baseline methods and offering a strong balance between privacy protection and model utility.

Abstract

Publishing private face recognition~(FR) training datasets is privacy-sensitive because faces expose identity information. Private FR training dataset publication mitigates this risk by releasing protected proxies as substitutes for private training faces. However, training FR models with such data introduces an identity paradox: \emph{the identity cues that make released faces useful for recognition supervision are also the cues that make them linkable to real individuals.} A protected face should be decoupled from the original identity, yet still behave as a reliable identity sample for training. Removing these cues too aggressively may destroy the class structure needed for recognition learning, whereas preserving them too faithfully may increase source-identity linkability. We argue that this paradox stems from conflating source-aligned identity semantics with recognition-useful proxy identity geometry. The former should be suppressed to reduce linkage to private individuals, while the latter should be preserved for FR learning. Based on this insight, we propose \textbf{Private Face Distillation}, an identity-decoupling and geometry-preserving framework. It uses Orthogonal Geometry Preservation to construct decoupled proxy identities from private identity representations while maintaining hyperspherical geometry, and Relational Topology Alignment to preserve identity relations for recognition learning. Experiments across multiple domain-shifted FR scenarios show that Private Face Distillation achieves stronger utility than the evaluated publication baselines. On IJB-C surveillance, it improves $\mathrm{TAR}@\mathrm{FAR}{=}1\text{e-}{3}$ by 3.94\% over the baseline while reducing source-identity linkability. These results suggest that private FR training dataset publication should decouple source-identity correspondence while preserving proxy identity geometry.

Problem

• Private Face Recognition
• Dataset Publication
• Identity Privacy
• Face Distillation
• Identity Decoupling

Innovation

• Private Face Distillation
• Identity Decoupling
• Geometry Preservation
• Face Recognition
• Privacy-Preserving Dataset Publication