An Empirical Comparison of Individual Machine Learning Techniques in Signature and Fingerprint Classification

da Costa-Abreu, Marjory and Fairhurst, Michael (2008) An Empirical Comparison of Individual Machine Learning Techniques in Signature and Fingerprint Classification. In: Schouten, Ben and Juul, Niels Christian and Drygajlo, Andrzej and Tistarelli, Massimo, eds. Biometrics and Identity Management: BOID 2008 (1st : 2008 : Roskilde, Denmark). Lecture Notes in Computer Science, 5372 . Springer, Berlin, pp. 130-139. ISBN 978-3-540-89990-7 ISSN 0302-9743. (doi:10.1007/978-3-540-89991-4_14) (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) (KAR id:23128)

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.
Official URL: https://doi.org/10.1007/978-3-540-89991-4_14

Abstract

This paper describes an empirical study to investigate the performance of a wide range of classifiers deployed in applications to classify biometric data. The study specifically reports results based on two different modalities, the handwritten signature and fingerprint recognition. We demonstrate quantitatively how performance is related to classifier type, and also provide a finer-grained analysis to relate performance to specific non-biometric factors in population demographics. The paper discusses the implications for individual modalities, for multiclassifier but single modality systems, and for full multibiometric solutions.

Item Type:	Book section
DOI/Identification number:	10.1007/978-3-540-89991-4_14
Additional information:	First European Workshop on Biometrics and Identity Management (BIOID2008, Roskilde University, Denmark, 7-9 May 2008
Uncontrolled keywords:	Classifiers, signature, fingerprints
Subjects:	T Technology > TK Electrical engineering. Electronics. Nuclear engineering > TK7800 Electronics > TK7880 Applications of electronics > TK7882.B56 Biometric identification
Divisions:	Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User:	J. Harries
Date Deposited:	10 Mar 2010 14:27 UTC
Last Modified:	05 Nov 2024 10:02 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/23128 (The current URI for this page, for reference purposes)

University of Kent Author Information

da Costa-Abreu, Marjory.

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Fairhurst, Michael.

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