Deep face template protection in the wild

Sunpill Kim, Hoyong Shin, Jae Hong Seo

Abstract: The advancement in the field of deep neural network (NN) leads practical recognition systems for biometrics such as face but also increases the threat to privacy such as recovering original biometrics from templates. The efficiency, the security and the usability are three points of important but difficult-to-achieve simultaneously in template protection. IronMask (CVPR 2021) shows the importance of efficient error-correcting mechanism on the metric used in the recognition system when designing template protection satisfying these three points at the same time. It is a first modular protection that can be added to any NN-based face recognition system independently (pre)trained by metric learning with cosine similarity. In addition, its performance with three datasets (Multi-PIE, FEI, Color FERET), which are widely used for evaluating template protection, is comparable with protection-recognition integrated systems that limit the usability due to inefficient registration. In this paper, we first demonstrate and analyze limit of IronMask by using more wilder and larger face datasets (LFW, AgeDB-30, CFP-FP, IJB-C). On the basis of our analyses on IronMask, we propose a new face template protection that has several benefits over IronMask with preserving modular feature. First, ours provides more flexibility to manipulate the error-correcing capacity for balancing between true accept rate (TAR) and false accept rate (FAR). Second, ours minimizes performance degradation while keeping appropriate level of security; even evaluating with a large dataset IJB-C, we achieve a TAR of 96.31% at a FAR of 0.05% with 118-bit security when combined with ArcFace that achieves 96.97% TAR at 0.01% FAR.

External IDs:dblp:journals/pr/KimSS25