Two-dimensional winner-takes-all hashing in template protection based on fingerprint and voice feature level fusion

Abstract

Biometrics has been explosively deployed for identity verification and/or identification over the last decade. Lately, multi-biometric systems are gaining attention due to its universality and higher accuracy in biometric recognition. However, the compromise of templates stored in database as separate entities in multi-biometric systems undoubtedly poses the major security and privacy threats due to the strong binding between identity and biometric data. In this paper, we propose to fuse fingerprint and voice modalities at feature level to obtain an integrated template. Subsequently, we propose two-dimensional Winner-Takes-All hashing method to protect the fused template. The proposed hashing method is inspired from Winner-Takes-All hashing and further altered for this unique multi-biometric system. Specifically, the proposed hashing method transforms the continuous fused biometric feature into discrete value. Such transformation enjoys strong non-linearity and thus resilient to the feature variation in certain degree. We show that the resultant hashed code can withstand the major attacks (e.g. template invertibility attack, attack via multiplicity etc.) while yielding reasonable recognition performance. A low equal error rate of 0.94% is obtained using the proposed hashing method on fingerprint images from FVC2002 DB1 and FVC2002 DB2 datasets and voice features from NIST Speaker Recognition Evaluation (SRE) 2004 ∼ 2010. More importantly, the proposed two-dimensional Winner-Takes-All hashing method can be extended and applied to other biometric modalities with real value representation.