Enhanced on-line signature verification based on skilled forgery detection using Sigma-LogNormal Features
Entity
UAM. Departamento de Tecnología Electrónica y de las ComunicacionesPublisher
Institute of Electrical and Electronics EngineersDate
2015Citation
10.1109/ICB.2015.7139065
International Conference on Biometrics, ICB 2015. IEEE, 2015. 501-506
ISBN
978-1-4799-7824-3DOI
10.1109/ICB.2015.7139065Funded by
This work has been partially supported by project Bio- Shield (TEC2012-34881) from Spanish MINECO, BEAT (FP7-SEC-284989) from EU, Cátedra UAM-Telefónica, CECABANK, and grant RGPIN-915 from NSERC Canada. M. G.-B. is supported by a FPU Fellowship from Spanish MECD.Project
info:eu-repo/grantAgreement/EC/FP7/284989Editor's Version
http://dx.doi.org/10.1109/ICB.2015.7139065Subjects
Computational modeling; Databases; Detectors; Informática; TelecomunicacionesNote
Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. M. Gomez-Barrero, J. Galbally, J. Fierrez, and J. Ortega-Garcia, "Enhanced on-line signature verification based on skilled forgery detection using Sigma-LogNormal Features", in International Conference on Biometrics, ICB 2015, 501-506Rights
© 2015 IEEEAbstract
One of the biggest challenges in on-line signature verification is the detection of skilled forgeries. In this paper, we propose a novel scheme, based on the Kinematic Theory of rapid human movements and its associated Sigma LogNormal model, to improve the performance of on-line signature verification systems. The approach combines the high performance of DTW-based systems in verification tasks, with the high potential for skilled forgery detection of the Kinematic Theory of rapid human movements. Experiments were carried out on the publicly available BiosecurID multimodal database, comprising 400 subjects. Results show that the performance of the DTW-based system improves for both skilled and random forgeries.
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Google Scholar:Gómez-Barrero, Marta
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Galbally Herrero, Javier
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Fiérrez Aguilar, Julián
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Ortega García, Javier
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