Feature selection based on genetic algorithms for on-line signature verification

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dc.contributor.author Galbally Herrero, Javier
dc.contributor.author Freire, Manuel R.
dc.contributor.author Ortega-García, Javier
dc.contributor.author Fiérrez, Julián
dc.contributor.other UAM. Departamento de Ingeniería Informática es_ES
dc.date.accessioned 2015-01-20T18:46:54Z
dc.date.available 2015-01-20T18:46:54Z
dc.date.issued 2007-10
dc.identifier.citation 2007 IEEE Workshop on Automatic Identification Advanced Technologies. IEEE, 2007. 198 - 203 en_US
dc.identifier.isbn 1-4244-1300-1
dc.identifier.uri http://hdl.handle.net/10486/663232
dc.description 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. J. Galbally, J. Fiérrez, M. R. Freire, J. Ortega-garcía, "Feature Selection Based on Genetic Algorithms for On-Line Signature Verification" in Workshop on Automatic Identification Advanced Technologies, 2007, 198 - 203. en_US
dc.description.abstract Two different genetic algorithm (GA) architectures are applied to a feature selection problem in on-line signature verification. The standard GA with binary coding is first used to find a suboptimal subset of features that minimizes the verification error rate of the system. The curse of dimensionality phenomenon is further investigated using a GA with integer coding. Results are given on the MCYT signature database comprising 330 users (16500 signatures). Signatures are represented by means of a set of 100 features which can be divided into four different groups according to the signature information they contain, namely: i) time, ii) speed and acceleration, iii) direction, and iv) geometry. The GA indicates that features from subsets i and iv are the most discriminative when dealing with random forgeries, while parameters from subsets ii and iv are the most appropriate to maximize the recognition rate with skilled forgeries. en_US
dc.description.sponsorship This work was supported by Spanish MEC under project TEC2006-13141-C03-03 and the European NoE Biosecure. en_US
dc.format.extent 7 pág. es_ES
dc.format.mimetype application/pdf en
dc.language.iso eng en
dc.publisher IEEE en_US
dc.relation.ispartof IEEE Workshop on Automatic Identification Advanced Technologies - Proceedings en_US
dc.rights © 2007 IEEE en_US
dc.subject.other Binary codes en_US
dc.subject.other Feature extraction en_US
dc.subject.other Genetic algorithms en_US
dc.subject.other Handwriting recognition en_US
dc.subject.other Image recognition en_US
dc.subject.other Acceleration en_US
dc.subject.other Biometrics en_US
dc.subject.other Convergence en_US
dc.subject.other Error analysis en_US
dc.subject.other Feature extraction en_US
dc.subject.other Forgery en_US
dc.subject.other Information geometry en_US
dc.subject.other Spatial databases en_US
dc.title Feature selection based on genetic algorithms for on-line signature verification en_US
dc.type conferenceObject en
dc.type bookPart en
dc.subject.eciencia Informática es_ES
dc.relation.publisherversion http://dx.doi.org/10.1109/AUTOID.2007.380619
dc.identifier.doi 10.1109/AUTOID.2007.380619
dc.identifier.publicationfirstpage 198
dc.identifier.publicationlastpage 203
dc.relation.eventdate June 7-8, 2007 en_US
dc.relation.eventplace Alghero (Italy) en_US
dc.relation.eventtitle IEEE Workshop on Automatic Identification Advanced Technologies, AUTOID 2007 en_US
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP6/507634 en
dc.type.version info:eu-repo/semantics/acceptedVersion en
dc.contributor.group Análisis y Tratamiento de Voz y Señales Biométricas (ING EPS-002) es_ES
dc.rights.accessRights openAccess en
dc.authorUAM Fierrez Aguilar, Julián (261834)
dc.authorUAM Galbally Herrero, Javier (261846)
dc.authorUAM Freire Santos, Manuel Ricardo (264091)


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