Towards better and unlinkable protected biometric templates using label‐assisted discrete hashing

Abstract

Abstract The growth of biometrics‐based authentication in various services raises the need to protect biometric data at the storage level. Specifically, biometric templates need to be protected after features are extracted to avoid the leakage of biometric data and subsequent linkability issues. An approach based on discrete hashing is presented with the assistance of semantic labels to generate discriminative and privacy preserving protected templates in this work. The proposed approach can easily be adopted for a closed‐enrolment set in which enrolment images are known a priori whereas the challenge of learning templates for a single subject remains open. To extend this approach for individual subject, the concept of auxiliary pseudouser enrolment data is introduced, through which a protected template can be generated at the user level. Through the use of a moderately sized multimodal biometric database of 94 subjects, the effectiveness of the proposed approach is illustrated to achieve a robust and secure template protection with irreversibility, unlinkability and renewability. With the set of experiments, the performance of the template protection approach is established and benchmarked against the popular bloom‐filter technique. The proposed approach results in a high genuine match rate (≈100% at a false accept rate of 0.01%) and low equal error rate (EER ≈ 0%) and outperforms traditional approaches while satisfying other requirements of biometric template protection when the closed enrolment set is known. With auxiliary pseudousers, the performance of the proposed approach for user‐level protected template creation results in an EER of 2.5%, indicating very low performance degradation compared with the known enrolment dataset. Along with the set of experimental validation of the proposed approach, a security analysis of the proposed approach is presented to demonstrate the unlinkability of the biometric templates using a state‐of‐art unlinkability metric.