Remarks on BioHashing based cancelable biometrics in verification system

Biometric characteristics are immutable and hence their compromise is permanent. To address this problem, cancelable biometrics was introduced to denote biometric templates that can be canceled and replaced. BioHash is a form of cancelable biometrics which mixes a set of user-specific random numbers...

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Main Authors: JIN, A, CONNIE, T
Format: Article
Language:English
Published: ELSEVIER SCIENCE BV 2006
Subjects:
Online Access:http://shdl.mmu.edu.my/3248/
http://shdl.mmu.edu.my/3248/1/1303.pdf
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author JIN, A
CONNIE, T
author_facet JIN, A
CONNIE, T
author_sort JIN, A
building MMU Institutional Repository
collection Online Access
description Biometric characteristics are immutable and hence their compromise is permanent. To address this problem, cancelable biometrics was introduced to denote biometric templates that can be canceled and replaced. BioHash is a form of cancelable biometrics which mixes a set of user-specific random numbers with the biometric features. The main drawback of BioHash is its great degradation in performance when the legitimate token is stolen and used by the imposter to claim as the legitimate user. In this paper, we employ a modified probabilistic neural network as the classifier to alleviate this problem. The experiments are tested on the FERET face data set with promising results. (c) 2006 Elsevier B.V. All rights reserved.
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spelling mmu-32482014-03-03T04:50:43Z http://shdl.mmu.edu.my/3248/ Remarks on BioHashing based cancelable biometrics in verification system JIN, A CONNIE, T T Technology (General) QA75.5-76.95 Electronic computers. Computer science Biometric characteristics are immutable and hence their compromise is permanent. To address this problem, cancelable biometrics was introduced to denote biometric templates that can be canceled and replaced. BioHash is a form of cancelable biometrics which mixes a set of user-specific random numbers with the biometric features. The main drawback of BioHash is its great degradation in performance when the legitimate token is stolen and used by the imposter to claim as the legitimate user. In this paper, we employ a modified probabilistic neural network as the classifier to alleviate this problem. The experiments are tested on the FERET face data set with promising results. (c) 2006 Elsevier B.V. All rights reserved. ELSEVIER SCIENCE BV 2006-10 Article NonPeerReviewed text en http://shdl.mmu.edu.my/3248/1/1303.pdf JIN, A and CONNIE, T (2006) Remarks on BioHashing based cancelable biometrics in verification system. Neurocomputing, 69 (16-18). pp. 2461-2464. ISSN 09252312 http://dx.doi.org/10.1016/j.neucom.2006.01.024 doi:10.1016/j.neucom.2006.01.024 doi:10.1016/j.neucom.2006.01.024
spellingShingle T Technology (General)
QA75.5-76.95 Electronic computers. Computer science
JIN, A
CONNIE, T
Remarks on BioHashing based cancelable biometrics in verification system
title Remarks on BioHashing based cancelable biometrics in verification system
title_full Remarks on BioHashing based cancelable biometrics in verification system
title_fullStr Remarks on BioHashing based cancelable biometrics in verification system
title_full_unstemmed Remarks on BioHashing based cancelable biometrics in verification system
title_short Remarks on BioHashing based cancelable biometrics in verification system
title_sort remarks on biohashing based cancelable biometrics in verification system
topic T Technology (General)
QA75.5-76.95 Electronic computers. Computer science
url http://shdl.mmu.edu.my/3248/
http://shdl.mmu.edu.my/3248/
http://shdl.mmu.edu.my/3248/
http://shdl.mmu.edu.my/3248/1/1303.pdf