Update Strategies for HMM-Based Dynamic Signature Biometric Systems
Entity
UAM. Departamento de Tecnología Electrónica y de las ComunicacionesPublisher
Institute of Electrical and Electronics Engineers Inc.Date
2015Citation
10.1109/WIFS.2015.7368583
2015 IEEE International Workshop on Information Forensics and Security (WIFS). IEEE, 2015. 7368583
ISBN
978-1-4673-6802-5DOI
10.1109/WIFS.2015.7368583Funded by
This work was supported in part by the Project Bio-Shield (TEC2012-34881), in part by Cecabank e-BioFirma Contract, in part by the BEAT Project (FP7-SEC-284989) and in part by Catedra UAM-Telefonica.Project
Gobierno de España. TEC2012-34881; info:eu-repo/grantAgreement/EC/FP7/284989Editor's Version
http://dx.doi.org/10.1109/WIFS.2015.7368583Subjects
Biometrics; Dynamic signature; GMM; HMM; Signature Long-Term database; System configuration update; Time functions-based system; 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. [R. Tolosana, R. Vera-Rodriguez, J. Ortega-Garcia and J. Fierrez, "Update strategies for HMM-based dynamic signature biometric systems," Information Forensics and Security (WIFS), 2015 IEEE International Workshop on, Rome, 2015, pp. 1-6. doi: 10.1109/WIFS.2015.7368583Rights
© 2015 IEEEAbstract
Biometric authentication on devices such as smart-
phones and tablets has increased significantly in the last years.
One of the most acceptable and increasing traits is the handwrit-
ing signature as it has been used in financial and legal agreements
scenarios for over a century. Nowadays, it is frequent to sign in
banking and commercial areas on digitizing tablets. For these
reasons, it is necessary to consider a new scenario where the
number of training signatures available to generate the user
template is variable and besides it has to be taken into account
the lap of time between them (inter-session variability). In this
work we focus on dynamic signature verification. The main goal
of this work is to study system configuration update strategies
of time functions-based systems such as Hidden Markov Model
(HMM) and Gaussian Mixture Models (GMM). Therefore, two
different cases have been considered. First, the usual case of
having an HMM-based system with a fixed configuration (i.e.
Baseline System). Second, an HMM-based and GMM-based sys-
tems whose configurations are optimized regarding the number
of training signatures available to generate the user template. The
experimental work has been carried out using an extended version
of the Signature Long-Term database taking into account skilled
and random or zero-effort forgeries. This database is comprised
of a total of 6 different sessions distributed in a 15-month time
span. Analyzing the results, the Proposed Systems achieve an
average absolute improvement of 4.6% in terms of EER(%) for
skilled forgeries cases compared to the Baseline System whereas
the average absolute improvement for the random forgeries cases
is of 2.7% EER. These results show the importance of optimizing
the configuration of the systems compared to a fixed configuration
system when the number of training signatures available to
generate the user template increases.
Files in this item
Google Scholar:Tolosana Moranchel, Rubén
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Vera Rodríguez, Rubén
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Ortega García, Javier
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Fiérrez Aguilar, Julián
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