Flexible Secure Biometrics: A Protected Modality-Invariant Face-Periocular Recognition System

Tiong-Sik Ng, Jihyeon Kim, Andrew Beng Jin Teoh

Abstract: This paper introduces Flexible Secure Biometrics (FSB), a novel learning framework that protects biometric templates across face-periocular modalities in intra- and cross-modality recognition tasks. The increasing flexibility of biometric recognition systems, which can match multiple template modalities, also escalates the security risks of tampering and misuse. To address these challenges, we propose the FSB-HashNet architecture, which integrates two key components: a periocular-face feature extractor and an adversarial hash generator. The feature extractor identifies and emphasizes shared prominent features between periocular and face modalities, creating modality-invariant representations. Meanwhile, the adversarial network simultaneously generates secure hash codes and ensures alignment across different modalities, preserving modality-invariant characteristics. The FSB-HashNet employs a two-factor protection mechanism using a subject’s biometric data and a user-specific key, resulting in robust, protected hash codes that offer image-level security without compromising recognition performance. Our comprehensive experiments on diverse, in-the-wild datasets under open-set conditions demonstrate the framework’s ability to maintain key security properties—unlinkability, revocability, and non-invertibility while preserving decent recognition accuracy. Codes are publicly available at https://github.com/tiongsikng/fsb_hashnet

External IDs:dblp:journals/tifs/NgKT25