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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/4796
Ver los metadatos del registro completo
id |
CONICETDig_5a6d67d80dc45f9be28129334349342d |
---|---|
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 |