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17070 Fingerprint Verification Using Spectral Minutiae Representations
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Xu, Haiyun and Veldhuis, R.N.J. and Bazen, A.M. and Kevenaar, T.A.M. and Akkermans, A.H.M. and Gökberk, B. (2009) Fingerprint Verification Using Spectral Minutiae Representations. IEEE Transactions on Information Forensics and Security, 4 (3). pp. 397-409. ISSN 1556-6013 *** ISI Impact 2,338 ***

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Official URL: http://dx.doi.org/10.1109/TIFS.2009.2021692

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Abstract

Most fingerprint recognition systems are based on the use of a minutiae set, which is an unordered collection of minutiae locations and orientations suffering from various deformations such as translation, rotation, and scaling. The spectral minutiae representation introduced in this paper is a novel method to represent a minutiae set as a fixed-length feature vector, which is invariant to translation, and in which rotation and scaling become translations, so that they can be easily compensated for. These characteristics enable the combination of fingerprint recognition systems with template protection schemes that require a fixed-length feature vector. This paper introduces the concept of algorithms for two representation methods: the location-based spectral minutiae representation and the orientation-based spectral minutiae representation. Both algorithms are evaluated using two correlation-based spectral minutiae matching algorithms. We present the performance of our algorithms on three fingerprint databases. We also show how the performance can be improved by using a fusion scheme and singular points.

Item Type:Article
Research Group:EWI-SAS: Signals and Systems
Research Program:CTIT-ISTRICE: Integrated Security and Privacy in a Networked World
Research Project:ProBiTe: Protection of Biometric Templates, TURBINE: TrUsted Revocable Biometric IdeNtitiEs
Uncontrolled Keywords:Biometrics, Fingerprint recognition, minutiae, spectral minutiae, template protection
ID Code:17070
Status:Published
Deposited On:21 December 2009
Refereed:Yes
International:Yes
ISI Impact Factor:2,338
More Information:statisticsmetis

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