Binary Biometrics: An Analytic Framework to Estimate the Bit Error Probability under Gaussian Assumption

E.J.C. Kelkboom, G. Garcia Molina, T.A.M. Kevenaar, R.N.J. Veldhuis, W. Jonker

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

12 Citations (Scopus)
10 Downloads (Pure)

Abstract

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.
Original languageEnglish
Title of host publication2nd IEEE International Conference on Biometrics: Theory, Applications and Systems, BTAS 2008
Place of PublicationLos Alamitos
PublisherIEEE Computer Society Press
Pages1-6
Number of pages6
ISBN (Print)978-1-4244-2729-1
DOIs
Publication statusPublished - 29 Sep 2008
Event2nd IEEE International Conference on Biometrics: Theory Applications and Systems, BTAS 2008 - Washington, United States
Duration: 29 Sep 20081 Oct 2008
Conference number: 2
http://www.cse.nd.edu/BTAS_08/

Conference

Conference2nd IEEE International Conference on Biometrics: Theory Applications and Systems, BTAS 2008
Abbreviated titleBTAS
CountryUnited States
City Washington
Period29/09/081/10/08
Internet address

Keywords

  • SCS-Safety
  • CR-D.4.6
  • METIS-256133
  • EWI-14616
  • IR-65219
  • DB-SDM: SECURE DATA MANAGEMENT

Fingerprint Dive into the research topics of 'Binary Biometrics: An Analytic Framework to Estimate the Bit Error Probability under Gaussian Assumption'. Together they form a unique fingerprint.

Cite this