Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/60100
Campo DC Valoridioma
dc.contributor.authorMarin-Reyes, Pedro A.en_US
dc.contributor.authorIrigoien, Itziaren_US
dc.contributor.authorSierra, Basilioen_US
dc.contributor.authorLorenzo-Navarro, Javieren_US
dc.contributor.authorCastrillon-Santana, Modestoen_US
dc.contributor.authorArenas, Concepcionen_US
dc.date.accessioned2020-01-14T08:45:35Z-
dc.date.available2020-01-14T08:45:35Z-
dc.date.issued2019en_US
dc.identifier.issn2073-8994en_US
dc.identifier.otherWoS-
dc.identifier.urihttp://hdl.handle.net/10553/60100-
dc.description.abstractTransparency laws facilitate citizens to monitor the activities of political representatives. In this sense, automatic or manual diarization of parliamentary sessions is required, the latter being time consuming. In the present work, this problem is addressed as a person re-identification problem. Re-identification is defined as the process of matching individuals under different camera views. This paper, in particular, deals with open world person re-identification scenarios, where the captured probe in one camera is not always present in the gallery collected in another one, i.e., determining whether the probe belongs to a novel identity or not. This procedure is mandatory before matching the identity. In most cases, novelty detection is tackled applying a threshold founded in a linear separation of the identities. We propose a threshold-less approach to solve the novelty detection problem, which is based on a one-class classifier and therefore it does not need any user defined threshold. Unlike other approaches that combine audio-visual features, an Isometric LogRatio transformation of a posteriori (ILRA) probabilities is applied to local and deep computed descriptors extracted from the face, which exhibits symmetry and can be exploited in the re-identification process unlike audio streams. These features are used to train the one-class classifier to detect the novelty of the individual. The proposal is evaluated in real parliamentary session recordings that exhibit challenging variations in terms of pose and location of the interveners. The experimental evaluation explores different configuration sets where our system achieves significant improvement on the given scenario, obtaining an average F measure of 71.29% for online analyzed videos. In addition, ILRA performs better than face descriptors used in recent face-based closed world recognition approaches, achieving an average improvement of 1.6% with respect to a deep descriptor.en_US
dc.languageengen_US
dc.relationIdentificación Automática de Oradores en Sesiones Parlamentarias Usando Características Audiovisuales.en_US
dc.relation.ispartofSymmetryen_US
dc.sourceSymmetry[ISSN 2073-8994],v. 11 (9), 1154en_US
dc.subject120304 Inteligencia artificialen_US
dc.subject.otherRe-identificationen_US
dc.subject.otherOpen world scenarioen_US
dc.subject.otherNovelty detectionen_US
dc.subject.otherOne-class classificationen_US
dc.subject.otherILR transformationen_US
dc.subject.otherLocal descriptorsen_US
dc.subject.otherDeep descriptoren_US
dc.titleILRA: novelty detection in face-based intervener re-identificationen_US
dc.typeinfo:eu-repo/semantics/Articleen_US
dc.typeArticleen_US
dc.identifier.doi10.3390/sym11091154en_US
dc.identifier.isi000489177900090-
dc.identifier.eissn2073-8994-
dc.identifier.issue9-
dc.relation.volume11en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.contributor.daisngid15775956-
dc.contributor.daisngid31707979-
dc.contributor.daisngid406165-
dc.contributor.daisngid34923785-
dc.contributor.daisngid32145428-
dc.contributor.daisngid657403-
dc.utils.revisionen_US
dc.contributor.wosstandardWOS:Marin-Reyes, PA-
dc.contributor.wosstandardWOS:Irigoien, I-
dc.contributor.wosstandardWOS:Sierra, B-
dc.contributor.wosstandardWOS:Lorenzo-Navarro, J-
dc.contributor.wosstandardWOS:Castrillon-Santana, M-
dc.contributor.wosstandardWOS:Arenas, C-
dc.date.coverdateSeptiembre 2019en_US
dc.identifier.ulpgcen_US
dc.contributor.buulpgcBU-INFen_US
dc.description.sjr0,365
dc.description.jcr2,143
dc.description.sjrqQ2
dc.description.jcrqQ2
dc.description.scieSCIE
item.grantfulltextopen-
item.fulltextCon texto completo-
crisitem.author.deptGIR SIANI: Inteligencia Artificial, Robótica y Oceanografía Computacional-
crisitem.author.deptIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.deptGIR SIANI: Inteligencia Artificial, Robótica y Oceanografía Computacional-
crisitem.author.deptIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.orcid0000-0002-2834-2067-
crisitem.author.orcid0000-0002-8673-2725-
crisitem.author.parentorgIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.parentorgIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.fullNameLorenzo Navarro, José Javier-
crisitem.author.fullNameCastrillón Santana, Modesto Fernando-
crisitem.project.principalinvestigatorCastrillón Santana, Modesto Fernando-
Colección:Artículos
miniatura
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