Binary Biometrics: An Analytic Framework to Estimate the Performance Curves Under Gaussian Assumption
Kelkboom, Emile J.C. and Garcia Molina, Gary and Breebaart, Jeroen and Veldhuis, Raymond N.J. and Kevenaar, Tom A.M. and Jonker, Willem (2010) Binary Biometrics: An Analytic Framework to Estimate the Performance Curves Under Gaussian Assumption. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 40 (3). 555 571. ISSN 10834427

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Abstract:  In recent years, the protection of biometric data has gained increased interest from the scientific community. Methods such as the fuzzy commitment scheme, helperdata system, fuzzy extractors, fuzzy vault, and cancelable biometrics have been proposed for protecting biometric data. Most of these methods use cryptographic primitives or errorcorrecting codes (ECCs) and use a binary representation of the realvalued biometric data. Hence, the difference between two biometric samples is given by the Hamming distance (HD) or bit errors between the binary vectors obtained from the enrollment and verification phases, respectively. If the HD is smaller (larger) than the decision threshold, then the subject is accepted (rejected) as genuine. Because of the use of ECCs, this decision threshold is limited to the maximum errorcorrecting capacity of the code, consequently limiting the false rejection rate (FRR) and false acceptance rate tradeoff. A method to improve the FRR consists of using multiple biometric samples in either the enrollment or verification phase. The noise is suppressed, hence reducing the number of bit errors and decreasing the HD. In practice, the number of samples is empirically chosen without fully considering its fundamental impact. In this paper, we present a Gaussian analytical framework for estimating the performance of a binary biometric system given the number of samples being used in the enrollment and the verification phase. The errordetection tradeoff curve that combines the false acceptance and false rejection rates is estimated to assess the system performance. The analytic expressions are validated using the Face Recognition Grand Challenge v2 and Fingerprint Verification Competition 2000 biometric databases. 
Item Type:  Article 
Copyright:  © 2010 IEEE 
Faculty:  Electrical Engineering, Mathematics and Computer Science (EEMCS) 
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Link to this item:  http://purl.utwente.nl/publications/71010 
Official URL:  http://dx.doi.org/10.1109/TSMCA.2010.2041657 
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