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Complexity-based Biometric Signature Verification

Tolosana, Ruben, Vera-Rodriguez, Ruben, Guest, Richard, Fierrez, Julian, Ortega-Garcia, Javier (2018) Complexity-based Biometric Signature Verification. In: 14th IAPR International Conference on Document Analysis and Recognition. 14th IAPR International Conference on Document Analysis and Recognition. . IEEE ISBN 978-1-5386-3587-2. E-ISBN 978-1-5386-3586-5. (doi:10.1109/ICDAR.2017.40) (KAR id:62955)

Abstract

On-line signature verification systems are mainly based on two approaches: feature- or time functions-based systems (a.k.a. global and local systems). However, new sources of information can be also considered in order to complement these traditional approaches, reduce the intra-class variability and achieve more robust signature verification systems against forgers. In this paper we focus on the use of the concept of complexity in on-line signature verification systems. The main contributions of the present work are: 1) classification of users according to the complexity level of their signatures using features extracted from the Sigma LogNormal writing generation model, and 2) a new architecture for signature verification exploiting signature complexity that results in highly improved performance. Our proposed approach is tested considering the BiosecurID on-line signature database with a total of 400 users. Results of 5.8% FRR for a FAR = 5.0% have been achieved against skilled forgeries outperforming recent related works. In addition, an analysis of the optimal time functions for each complexity level is performed providing practical insights for the application of signature verification in real scenarios.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/ICDAR.2017.40
Subjects: Q Science
T Technology
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Richard Guest
Date Deposited: 25 Aug 2017 15:18 UTC
Last Modified: 08 Dec 2022 23:45 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/62955 (The current URI for this page, for reference purposes)

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