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Classifier ensembles and optimisation techniques to improve the performance of cancellable fingerprints

Canuto, Anne, Fairhurst, Michael, Pintro, Fernando, Xavier Junior, J.C, Neto, Antonino Feitosa, Goncalves, Luis Marcos G. (2011) Classifier ensembles and optimisation techniques to improve the performance of cancellable fingerprints. International Journal of Hybrid Intelligent Systems, 8 (3). pp. 143-154. ISSN 1448-5869. (doi:10.3233/HIS-2011-0135) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.3233/HIS-2011-0135

Abstract

The main aim of biometric-based identification systems is to automatically recognize individuals based on their physiological and/or behavioural characteristics such as fingerprint, face, hand-geometry, among others. These systems offer several advantages over traditional forms of identity protection. However, there are still some important aspects that need to be addressed in these systems. The main questions are concerned with the security of biometric authentication systems since it is important to ensure the integrity and public acceptance of these systems. In order to avoid the problems arising from compromised biometric templates, the concept of cancellable biometrics has recently been introduced. The concept is to transform a biometric trait into a new representation for enrolment and matching. Although cancellable biometrics were proposed to solve privacy concerns, the concept raises new issues, since they make the authentication problem more complex and difficult to solve. Thus, more effective authentication structures are needed to perform these tasks. In this paper, we investigate the use of ensemble systems in cancellable biometrics, using fingerprint-based identification to illustrate the possible benefits accruing. In order to increase the effectiveness of the proposed ensemble systems, three feature selection methods will be used to distribute the attributes among the individual classifiers of an ensemble. The main aim of this paper is to analyse the performance of such well-established structures on transformed biometric data to determine whether they have a positive effect on the performance of this complex and difficult task.

Item Type: Article
DOI/Identification number: 10.3233/HIS-2011-0135
Uncontrolled keywords: Classifier ensembles, selection-based combination methods, confidence measures
Subjects: T Technology
Divisions: Faculties > Sciences > School of Engineering and Digital Arts
Faculties > Sciences > School of Engineering and Digital Arts > Image and Information Engineering
Depositing User: Tina Thompson
Date Deposited: 22 Oct 2013 09:14 UTC
Last Modified: 29 May 2019 11:08 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/35567 (The current URI for this page, for reference purposes)
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