Offline Signature Verification System Based On The Online Data

dc.creatorZimmer A.
dc.creatorLing L.L.
dc.date2008
dc.date2015-06-30T19:16:04Z
dc.date2015-11-26T14:41:08Z
dc.date2015-06-30T19:16:04Z
dc.date2015-11-26T14:41:08Z
dc.date.accessioned2018-03-28T21:47:52Z
dc.date.available2018-03-28T21:47:52Z
dc.identifier
dc.identifierEurasip Journal On Advances In Signal Processing. , v. 2008, n. , p. - , 2008.
dc.identifier16876172
dc.identifier10.1155/2008/492910
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-43949084082&partnerID=40&md5=b7c31d378a30d8996103893f2c473496
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/105498
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/105498
dc.identifier2-s2.0-43949084082
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1250717
dc.descriptionMost of the signature verification work done in the past years focused either on offline or online approaches. In this paper, a different methodology is proposed, where the online reference data acquired through a digitizing tablet serves as the basis for the segmentation process of the corresponding scanned offline data. Local windows are built over the image through a self-adjustable learning process and are used to focus on the feature extraction step. The windows positions are determined according to the complexity of the underlying strokes based on the observation of a delta-lognormal handwritten reproduction model. Local features extraction that takes place focused on the windows formed, and it is used in conjunction with the global primitives to feed the classifier. The overall performance of the system is then measured with three different classification schemes.
dc.description2008
dc.description
dc.description
dc.description
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dc.languageen
dc.publisher
dc.relationEurasip Journal on Advances in Signal Processing
dc.rightsaberto
dc.sourceScopus
dc.titleOffline Signature Verification System Based On The Online Data
dc.typeArtículos de revistas


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