Iris authentication using privatized advanced correlation filter

Siew Chin Chong, Andrew Beng Jin Teoh, David Chek Ling Ngo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

22 Citations (Scopus)

Abstract

This paper proposes a private biometrics formulation which is based on the concealment of random kernel and the iris images to synthesize a minimum average correlation energy (MACE) filter for iris authentication. Specifically, we multiply training images with the user-specific random kernel in frequency domain before biometric filter is created. The objective of the proposed method is to provide private biometrics realization in iris authentication in which biometric template can be reissued once it was compromised. Meanwhile, the proposed method is able to decrease the computational load, due to the filter size reduction. It also improves the authentication rate significantly compare to the advance correlation based approach [5][6] and comparable to the Daugmant's Iris Code [1].

Original languageEnglish
Title of host publicationAdvances in Biometrics - International Conference, ICB 2006, Proceedings
Pages382-388
Number of pages7
Publication statusPublished - 2006
EventInternational Conference on Biometrics, ICB 2006 - Hong Kong, China
Duration: 2006 Jan 52006 Jan 7

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3832 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherInternational Conference on Biometrics, ICB 2006
CountryChina
CityHong Kong
Period06/1/506/1/7

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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    Chong, S. C., Teoh, A. B. J., & Ngo, D. C. L. (2006). Iris authentication using privatized advanced correlation filter. In Advances in Biometrics - International Conference, ICB 2006, Proceedings (pp. 382-388). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3832 LNCS).