Online Signature Verification: Automatic Feature Selection vs. FHEs Choice

Autores
Parodi, Marianela; Gomez, Juan Carlos; Alewijnse, Linda; Liwicki, Marcus
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this paper, the discriminative power of a set of features which seems to be relevant to signature analysis by Forensic Handwriting Experts (FHEs) is analysed and particularly compared to the discriminative power of automatically selected feature sets. This analysis could help FHEs to further understand the signatures and the writer behaviour. In addition, two information fusion schemes are proposed to combine the discriminative capability of the two types of features being considered. The coefficients in the wavelet decomposition of the different time functions associated with the signing process are used as features to model them. Two different signature styles are considered, namely,Western and Chinese, of one of the most recent publicly available Online Signature Databases. The experimental results are promising, especially for the features that seem to be relevant to FHEs, since the obtained verification error rates are comparable to the ones reported in the state-of-the-art over the same datasets. Further, the results also show that it is possible to combine both types of features to improve the verification performance.
Fil: Parodi, Marianela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina
Fil: Gomez, Juan Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina
Fil: Alewijnse, Linda. Netherlands Forensic Institute; Países Bajos
Fil: Liwicki, Marcus. German Research Center for Artificial Intelligence; Alemania
Materia
Forensic Document Examiners
Online Signature Verification
Fusion Techniques
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/4796
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/4796
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Online Signature Verification: Automatic Feature Selection vs. FHEs ChoiceParodi, MarianelaGomez, Juan CarlosAlewijnse, LindaLiwicki, MarcusForensic Document ExaminersOnline Signature VerificationFusion Techniqueshttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2In this paper, the discriminative power of a set of features which seems to be relevant to signature analysis by Forensic Handwriting Experts (FHEs) is analysed and particularly compared to the discriminative power of automatically selected feature sets. This analysis could help FHEs to further understand the signatures and the writer behaviour. In addition, two information fusion schemes are proposed to combine the discriminative capability of the two types of features being considered. The coefficients in the wavelet decomposition of the different time functions associated with the signing process are used as features to model them. Two different signature styles are considered, namely,Western and Chinese, of one of the most recent publicly available Online Signature Databases. The experimental results are promising, especially for the features that seem to be relevant to FHEs, since the obtained verification error rates are comparable to the ones reported in the state-of-the-art over the same datasets. Further, the results also show that it is possible to combine both types of features to improve the verification performance.Fil: Parodi, Marianela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; ArgentinaFil: Gomez, Juan Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; ArgentinaFil: Alewijnse, Linda. Netherlands Forensic Institute; Países BajosFil: Liwicki, Marcus. German Research Center for Artificial Intelligence; AlemaniaAssociation of Forensic Document Examiners2014-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/4796Parodi, Marianela; Gomez, Juan Carlos; Alewijnse, Linda; Liwicki, Marcus; Online Signature Verification: Automatic Feature Selection vs. FHEs Choice; Association of Forensic Document Examiners; Journal of Forensic Document Examination; 24; 5-2014; 5-190895-0849enginfo:eu-repo/semantics/altIdentifier/url/http://www.afde.org/journal.htmlinfo:eu-repo/semantics/altIdentifier/issn/0895-0849info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:57:06Zoai:ri.conicet.gov.ar:11336/4796instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 14:57:07.067CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Online Signature Verification: Automatic Feature Selection vs. FHEs Choice
title Online Signature Verification: Automatic Feature Selection vs. FHEs Choice
spellingShingle Online Signature Verification: Automatic Feature Selection vs. FHEs Choice
Parodi, Marianela
Forensic Document Examiners
Online Signature Verification
Fusion Techniques
title_short Online Signature Verification: Automatic Feature Selection vs. FHEs Choice
title_full Online Signature Verification: Automatic Feature Selection vs. FHEs Choice
title_fullStr Online Signature Verification: Automatic Feature Selection vs. FHEs Choice
title_full_unstemmed Online Signature Verification: Automatic Feature Selection vs. FHEs Choice
title_sort Online Signature Verification: Automatic Feature Selection vs. FHEs Choice
dc.creator.none.fl_str_mv Parodi, Marianela
Gomez, Juan Carlos
Alewijnse, Linda
Liwicki, Marcus
author Parodi, Marianela
author_facet Parodi, Marianela
Gomez, Juan Carlos
Alewijnse, Linda
Liwicki, Marcus
author_role author
author2 Gomez, Juan Carlos
Alewijnse, Linda
Liwicki, Marcus
author2_role author
author
author
dc.subject.none.fl_str_mv Forensic Document Examiners
Online Signature Verification
Fusion Techniques
topic Forensic Document Examiners
Online Signature Verification
Fusion Techniques
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv In this paper, the discriminative power of a set of features which seems to be relevant to signature analysis by Forensic Handwriting Experts (FHEs) is analysed and particularly compared to the discriminative power of automatically selected feature sets. This analysis could help FHEs to further understand the signatures and the writer behaviour. In addition, two information fusion schemes are proposed to combine the discriminative capability of the two types of features being considered. The coefficients in the wavelet decomposition of the different time functions associated with the signing process are used as features to model them. Two different signature styles are considered, namely,Western and Chinese, of one of the most recent publicly available Online Signature Databases. The experimental results are promising, especially for the features that seem to be relevant to FHEs, since the obtained verification error rates are comparable to the ones reported in the state-of-the-art over the same datasets. Further, the results also show that it is possible to combine both types of features to improve the verification performance.
Fil: Parodi, Marianela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina
Fil: Gomez, Juan Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina
Fil: Alewijnse, Linda. Netherlands Forensic Institute; Países Bajos
Fil: Liwicki, Marcus. German Research Center for Artificial Intelligence; Alemania
description In this paper, the discriminative power of a set of features which seems to be relevant to signature analysis by Forensic Handwriting Experts (FHEs) is analysed and particularly compared to the discriminative power of automatically selected feature sets. This analysis could help FHEs to further understand the signatures and the writer behaviour. In addition, two information fusion schemes are proposed to combine the discriminative capability of the two types of features being considered. The coefficients in the wavelet decomposition of the different time functions associated with the signing process are used as features to model them. Two different signature styles are considered, namely,Western and Chinese, of one of the most recent publicly available Online Signature Databases. The experimental results are promising, especially for the features that seem to be relevant to FHEs, since the obtained verification error rates are comparable to the ones reported in the state-of-the-art over the same datasets. Further, the results also show that it is possible to combine both types of features to improve the verification performance.
publishDate 2014
dc.date.none.fl_str_mv 2014-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/4796
Parodi, Marianela; Gomez, Juan Carlos; Alewijnse, Linda; Liwicki, Marcus; Online Signature Verification: Automatic Feature Selection vs. FHEs Choice; Association of Forensic Document Examiners; Journal of Forensic Document Examination; 24; 5-2014; 5-19
0895-0849
url http://hdl.handle.net/11336/4796
identifier_str_mv Parodi, Marianela; Gomez, Juan Carlos; Alewijnse, Linda; Liwicki, Marcus; Online Signature Verification: Automatic Feature Selection vs. FHEs Choice; Association of Forensic Document Examiners; Journal of Forensic Document Examination; 24; 5-2014; 5-19
0895-0849
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.afde.org/journal.html
info:eu-repo/semantics/altIdentifier/issn/0895-0849
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Association of Forensic Document Examiners
publisher.none.fl_str_mv Association of Forensic Document Examiners
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1846083107639263232
score 13.22299

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