Extracting biometric binary strings with minimal area under the FRR curve for the hamming distance classifier

Extracting binary strings from real-valued biometric templates is a fundamental step in template compression and protection systems, such as fuzzy commitment, fuzzy extractor, secure sketch and helper data systems. Quantization and coding are the straightforward way to extract binary representations from arbitrary real-valued biometric modalities. Afterwards, the binary strings can be compared by means of a Hamming distance classifier (HDC). One of the problems of the binary biometric representations is the allocation of quantization bits to the features. In this paper, we first give a theoretical model of the HDC, based on the features’ bit error probabilities after the quantization. This model predicts the false acceptance rate (FAR) and the false rejection rate (FRR) as a function of the Hamming distance threshold. Additionally, we propose the area under the FRR curve optimized bit allocation (AUF-OBA) principle. Given the features’ bit error probabilities, AUF-OBA assigns variable numbers of quantization bits to features, in such way that the analytical area under the FRR curve for the HDC is minimized. Experiments of AUF-OBA on the FVC2000 fingerprint database and the FRGC face database yield good verification performances. AUF-OBA is applicable to arbitrary biometric modalities, such as fingerprint texture, iris, signature and face.