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14616 Binary Biometrics: An Analytic Framework to Estimate the Bit Error Probability under Gaussian Assumption
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Kelkboom, E.J.C. and Garcia Molina, G. and Kevenaar, T.A.M. and Veldhuis, R.N.J. and Jonker, W. (2008) Binary Biometrics: An Analytic Framework to Estimate the Bit Error Probability under Gaussian Assumption. In: Biometrics: Theory, Applications and BTAS 2008. 2nd IEEE International Conference on Biometrics: Theory Applications and Systems, 2008., 29 Sep - 1 Oct 2008, Washington, USA. pp. 1-6. IEEE Computer Society. ISBN 978-1-4244-2729-1

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In recent years the protection of biometric data has gained increased interest from the scientific community. Methods such as the helper data system, fuzzy extractors, fuzzy vault and cancellable biometrics have been proposed for protecting biometric data. Most of these methods use cryptographic primitives and require a binary representation from the real-valued biometric data. Hence, the similarity of biometric samples is measured in terms of the Hamming distance between the binary vector obtained at the enrolment and verification phase. The number of errors depends on the expected error probability Pe of each bit between two biometric samples of the same subject. In this paper we introduce a framework for analytically estimating Pe under the assumption that the within-and between-class distribution can be modeled by a Gaussian distribution. We present the analytic expression of Pe as a function of the number of samples used at the enrolment (Ne) and verification (Nv) phases. The analytic expressions are validated using the FRGC v2 and FVC2000 biometric databases.

Item Type:Conference or Workshop Paper (Full Paper, Talk)
Research Group:EWI-DB: Databases, EWI-SAS: Signals and Systems
Research Program:CTIT-ISTRICE: Integrated Security and Privacy in a Networked World
Research Project:3D Face
ID Code:14616
Deposited On:10 January 2009
More Information:statisticsmetis

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