High security Iris verification system based on random secret integration

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

Research output: Contribution to journalArticle

60 Citations (Scopus)

Abstract

A dual-factor authentication methodology coined as S-Iris Encoding is proposed based on the iterated inner-products between the secret pseudo-random number and the iris feature, and with thresholding to produce a unique compact binary code per person. A thresholding method is devised to exclude the weak inner-product during the encoding process, and thus contribute to the improvement of performance. S-Iris Encoding is primary formulated based on the cancelable biometrics principle to protect against biometrics fabrication. The problem could be rectified by S-Iris code through the token replacement so that a new code can be generated instantly just as a new credit card number can be issued if the old one is compromised. Besides that, S-Iris code is non-invertible and can only contribute to the authentication process when both genuine biometrics template and token are presented. By applying S-Iris Encoding with weak inner-product exclusion, the original iris feature length can be greatly reduced to around 4% of the original size and a 0% of equal error rate (EER) can be attained in CASIA Iris image database.

Original languageEnglish
Pages (from-to)169-177
Number of pages9
JournalComputer Vision and Image Understanding
Volume102
Issue number2
DOIs
Publication statusPublished - 2006 May 1

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Biometrics
Authentication
Binary codes
Fabrication

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition

Cite this

Siew Chin, Chong ; Teoh, Beng Jin ; Chek Ling, David Ngo. / High security Iris verification system based on random secret integration. In: Computer Vision and Image Understanding. 2006 ; Vol. 102, No. 2. pp. 169-177.
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High security Iris verification system based on random secret integration. / Siew Chin, Chong; Teoh, Beng Jin; Chek Ling, David Ngo.

In: Computer Vision and Image Understanding, Vol. 102, No. 2, 01.05.2006, p. 169-177.

Research output: Contribution to journalArticle

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