4,015 research outputs found
HMM-based on-line signature verification: Feature extraction and signature modeling
This is the author’s version of a work that was accepted for publication in Pattern Recognition Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition Letters 28.16 (2007): 2325 – 2334, DOI: 10.1016/j.patrec.2007.07.012A function-based approach to on-line signature verification is presented. The system
uses a set of time sequences and Hidden Markov Models (HMMs). Development
and evaluation experiments are reported on a subcorpus of the MCYT bimodal
biometric database comprising more than 7,000 signatures from 145 subjects. The
system is compared to other state-of-the-art systems based on the results of the
First International Signature Verification Competition (SVC 2004). A number of
practical findings related to feature extraction and modeling are obtained.This work has been supported by the Spanish projects TIC2003-08382-C05-
01 and TEC2006-13141-C03-03, and by the European NoE Biosecure
Formal Dependability Engineering with MIOA
In this paper, we introduce MIOA, a stochastic process algebra-like specification language with datatypes, as well as a logic intSPDL, and its model checking algorithms. MIOA, which stands for Markovian input/output automata language, is an extension of Lynch's input/automata with Markovian timed transitions.MIOA can serve both as a fully fledged ``stand-alone'' specification language and the semantic model for the architectural dependability modelling and evaluation language Arcade. The logic intSPDL is an extension of the stochastic logic SPDL, to deal with the specialties of MIOA. intSPDL in the context of Arcade can be seen as the semantic model of abstract and complex dependability measures that can be defined in the Arcade framework. We define syntax and semantics of both MIOA and intSPDL, and show examples of applying MIOA and intSPDL in the realm of dependability modelling with Arcade
Update Strategies for HMM-Based Dynamic Signature Biometric Systems
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.2015.7368583Biometric 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.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
Automatic online signature verification using HMMs with user-dependent structure
Producción CientíficaA novel strategy for Automatic online Signature Verification based on hidden Markov models (HMM) with user-dependent structure is presented in this work. Under this approach, the number of states and Gaussians giving the optimal prediction results are independently selected for each user. With this simple strategy just three genuine signatures could be used for training, with an EER under 2.5% obtained for the basic set of raw signature parameters provided by the acquisition device. This results increment by a factor of six the accuracy obtained with the typical approach in which claim-independent structure is used for the HMMs.Ministerio de Educación y Formación Profesional (contract TIC2003-08382-C05-03)Junta de Castilla y Leon (project VA053A05
Feature Representation for Online Signature Verification
Biometrics systems have been used in a wide range of applications and have
improved people authentication. Signature verification is one of the most
common biometric methods with techniques that employ various specifications of
a signature. Recently, deep learning has achieved great success in many fields,
such as image, sounds and text processing. In this paper, deep learning method
has been used for feature extraction and feature selection.Comment: 10 pages, 10 figures, Submitted to IEEE Transactions on Information
Forensics and Securit
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