Biometric template protection for speaker recognition based on universal background models

(Voice-) biometric data is considered as personally identifiable information, that is, the increasing demand on (mobile) speaker recognition systems calls for applications which prevent from privacy threats, such as identity-theft or tracking without consent. Technologies of biometric template protection, in particular biometric cryptosystems, fulfil standardised properties of irreversibility and unlinkability which represent appropriate countermeasures to such vulnerabilities of conventional biometric recognition systems. Thereby, public confidence in and social acceptance of biometric applications is strengthened. In this work the authors propose a binarisation technique, which is used to extract scalable high-entropy binary voice reference data (templates) from speaker models, based on Gaussian mixture models and universal background models. Binary feature vectors are then protected within a template protection scheme in particular, fuzzy commitment scheme, in which error correction list-decoding is employed to overcome high intra-class variance of voice samples. In experiments, which are evaluated out on a text-independent speaker corpus of 339 individuals, it is demonstrated that the fully ISO/IEC IS 24745 compliant system achieves privacy protection at a negligible loss of biometric performance, confirming the soundness of the presented approach.