Towards efficient privacy-preserving two-stage identification for fingerprint-based biometric cryptosystems

Biometric template protection schemes in particular, biometric cryptosystems bind secret keys to biometric data, i.e. complex key retrieval processes are performed at each authentication attempt. Focusing on biometric identification exhaustive 1: N comparisons are required for identifying a biometric probe. As a consequence comparison time frequently dominates the overall computational workload, preventing biometric cryptosystems from being operated in identification mode. In this paper we propose a computational efficient two-stage identification system for fingerprint-biometric cryptosystems. Employing the concept of adaptive Bloom filter-based cancelable biometrics, pseudonymous binary prescreeners are extracted based on which top-candidates are returned from a database. Thereby the number of required key-retrieval processes is reduced to a fraction of the total. Experimental evaluations confirm that, by employing the proposed technique, biometric cryptosystems, e.g. fuzzy vault scheme, can be enhanced in order to enable a real-time privacy preserving identification, while at the same time biometric performance is maintained.