An a-contrario biometric fusion approach.
Resumen:
Fusion is a key component in many biometric systems: it is one of the most widely used techniques to improve their accuracy. Each time we need to combine the output of systems that use different biometric traits, or different samples of the same biometric trait, or even different algorithms, we need to define a fusion strategy. Independently of the fusion method used, there is always a decision step, in which it is decided if the traits being compared correspond to the same individual or not. In this work, we present a statistical decision criterion based on the a-contrario framework, which has already proven to be useful in biometric applications. The proposed method and its theoretical background is described in detail, and its application to biometric fusion is illustrated with simulated and real data.
| 2020 | |
| BIOMETRIA | |
| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/24382 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| _version_ | 1807522895819177984 |
|---|---|
| author | Di Martino, Luis |
| author2 | Preciozzi, Javier Grompone von Gioi, Rafael Garella, Guillermo Fernández, Alicia Lecumberry, Federico |
| author2_role | author author author author author |
| author_facet | Di Martino, Luis Preciozzi, Javier Grompone von Gioi, Rafael Garella, Guillermo Fernández, Alicia Lecumberry, Federico |
| author_role | author |
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| collection | COLIBRI |
| dc.contributor.filiacion.none.fl_str_mv | Di Martino Luis, Universidad de la República (Uruguay). Facultad de Ingeniería. Preciozzi Javier, Universidad de la República (Uruguay). Facultad de Ingeniería. Grompone von Gioi Rafael, Universite Paris-Saclay (France). Garella Guillermo, Universidad de la República (Uruguay). Facultad de Ingeniería. Fernández Alicia, Universidad de la República (Uruguay). Facultad de Ingeniería. Lecumberry Federico, Universidad de la República (Uruguay). Facultad de Ingeniería. |
| dc.creator.none.fl_str_mv | Di Martino, Luis Preciozzi, Javier Grompone von Gioi, Rafael Garella, Guillermo Fernández, Alicia Lecumberry, Federico |
| dc.date.accessioned.none.fl_str_mv | 2020-06-22T12:32:59Z |
| dc.date.available.none.fl_str_mv | 2020-06-22T12:32:59Z |
| dc.date.issued.none.fl_str_mv | 2020 |
| dc.description.abstract.none.fl_txt_mv | Fusion is a key component in many biometric systems: it is one of the most widely used techniques to improve their accuracy. Each time we need to combine the output of systems that use different biometric traits, or different samples of the same biometric trait, or even different algorithms, we need to define a fusion strategy. Independently of the fusion method used, there is always a decision step, in which it is decided if the traits being compared correspond to the same individual or not. In this work, we present a statistical decision criterion based on the a-contrario framework, which has already proven to be useful in biometric applications. The proposed method and its theoretical background is described in detail, and its application to biometric fusion is illustrated with simulated and real data. |
| dc.format.extent.es.fl_str_mv | 2 p. |
| dc.format.mimetype.es.fl_str_mv | application/pdf |
| dc.identifier.citation.en.fl_str_mv | Di Martino, L., Preciozzi, J., Grompone von Gioi, R., y otros. An a-contrario biometric fusion approach. En : 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops [en línea]. [s.l.] : IEEE-CVF, 2020. |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/24382 |
| dc.language.iso.none.fl_str_mv | en eng |
| dc.publisher.es.fl_str_mv | IEEE-CVF |
| dc.relation.ispartof.en.fl_str_mv | 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 14-19 Jun, pp. 822--823. |
| dc.rights.license.none.fl_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.other.es.fl_str_mv | BIOMETRIA |
| dc.title.none.fl_str_mv | An a-contrario biometric fusion approach. |
| dc.type.es.fl_str_mv | Artículo |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/article |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/publishedVersion |
| description | Fusion is a key component in many biometric systems: it is one of the most widely used techniques to improve their accuracy. Each time we need to combine the output of systems that use different biometric traits, or different samples of the same biometric trait, or even different algorithms, we need to define a fusion strategy. Independently of the fusion method used, there is always a decision step, in which it is decided if the traits being compared correspond to the same individual or not. In this work, we present a statistical decision criterion based on the a-contrario framework, which has already proven to be useful in biometric applications. The proposed method and its theoretical background is described in detail, and its application to biometric fusion is illustrated with simulated and real data. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | COLIBRI_4133c0cf493988af9008f79079a0d64a |
| identifier_str_mv | Di Martino, L., Preciozzi, J., Grompone von Gioi, R., y otros. An a-contrario biometric fusion approach. En : 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops [en línea]. [s.l.] : IEEE-CVF, 2020. |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | COLIBRI |
| network_name_str | COLIBRI |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/24382 |
| publishDate | 2020 |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | mabel.seroubian@seciu.edu.uy |
| repository.name.fl_str_mv | COLIBRI - Universidad de la República |
| repository_id_str | 4771 |
| rights_invalid_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| spelling | Di Martino Luis, Universidad de la República (Uruguay). Facultad de Ingeniería.Preciozzi Javier, Universidad de la República (Uruguay). Facultad de Ingeniería.Grompone von Gioi Rafael, Universite Paris-Saclay (France).Garella Guillermo, Universidad de la República (Uruguay). Facultad de Ingeniería.Fernández Alicia, Universidad de la República (Uruguay). Facultad de Ingeniería.Lecumberry Federico, Universidad de la República (Uruguay). Facultad de Ingeniería.2020-06-22T12:32:59Z2020-06-22T12:32:59Z2020Di Martino, L., Preciozzi, J., Grompone von Gioi, R., y otros. An a-contrario biometric fusion approach. En : 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops [en línea]. [s.l.] : IEEE-CVF, 2020.https://hdl.handle.net/20.500.12008/24382Fusion is a key component in many biometric systems: it is one of the most widely used techniques to improve their accuracy. Each time we need to combine the output of systems that use different biometric traits, or different samples of the same biometric trait, or even different algorithms, we need to define a fusion strategy. Independently of the fusion method used, there is always a decision step, in which it is decided if the traits being compared correspond to the same individual or not. In this work, we present a statistical decision criterion based on the a-contrario framework, which has already proven to be useful in biometric applications. The proposed method and its theoretical background is described in detail, and its application to biometric fusion is illustrated with simulated and real data.Submitted by Ribeiro Jorge (jribeiro@fing.edu.uy) on 2020-06-17T22:44:28Z No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) DPGGFL20.pdf: 882364 bytes, checksum: e6925b359bc27665d4c1389bd4c12c45 (MD5)Approved for entry into archive by Machado Jimena (jmachado@fing.edu.uy) on 2020-06-19T21:47:02Z (GMT) No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) DPGGFL20.pdf: 882364 bytes, checksum: e6925b359bc27665d4c1389bd4c12c45 (MD5)Made available in DSpace by Luna Fabiana (fabiana.luna@fic.edu.uy) on 2020-06-22T12:32:59Z (GMT). No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) DPGGFL20.pdf: 882364 bytes, checksum: e6925b359bc27665d4c1389bd4c12c45 (MD5) Previous issue date: 20202 p.application/pdfenengIEEE-CVF2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 14-19 Jun, pp. 822--823.Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad de la República.(Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. 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- Universidad de la Repúblicafalse |
| spellingShingle | An a-contrario biometric fusion approach. Di Martino, Luis BIOMETRIA |
| status_str | publishedVersion |
| title | An a-contrario biometric fusion approach. |
| title_full | An a-contrario biometric fusion approach. |
| title_fullStr | An a-contrario biometric fusion approach. |
| title_full_unstemmed | An a-contrario biometric fusion approach. |
| title_short | An a-contrario biometric fusion approach. |
| title_sort | An a-contrario biometric fusion approach. |
| topic | BIOMETRIA |
| url | https://hdl.handle.net/20.500.12008/24382 |
