Multimodal unconstrained people recognition with face and ear images using deep learning

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dc.contributor.advisorCamara Chavez, Guillermo
dc.contributor.authorRamos Cooper, Solange Griselly
dc.date.accessioned2023-11-15T16:25:34Z
dc.date.available2023-11-15T16:25:34Z
dc.date.issued2023
dc.description.abstractMultibiometric systems rely on the idea of combining multiple biometric methods into one single process that leads to a more reliable and accurate system. The combination of two different biometric traits such as face and ear results in an advantageous and complementary process when using 2D images taken under uncontrolled conditions. In this work, we investigate several approaches to fuse information from the face and ear images to recognize people in a more accurate manner than using each method separately. We leverage the research maturity level of the face recognition field to build, first a truly multimodal database of ear and face images called VGGFace-Ear dataset, second a model that can describe ear images with high generalization called VGGEar model, and finally explore fusion strategies at two different levels in a common recognition pipeline, feature and score levels. Experiments on the UERC dataset have shown, first of all, an improvement of around 7% compared to the state-of-the-art methods in the ear recognition field. Second, fusing information from the face and ear images increases recognition rates from 79% and 82%, in the unimodal face and ear recognition respectively, to 94% recognition rate using the Rank-1 metric.es_PE
dc.description.uriTesis de maestríaes_PE
dc.formatapplication/pdf
dc.identifier.other1080188
dc.identifier.urihttps://hdl.handle.net/20.500.12590/17819
dc.language.isoeng
dc.publisherUniversidad Católica San pablo
dc.publisher.countryPE
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.subjectMultibiometric systemes_PE
dc.subjectMultimodal recognitiones_PE
dc.subjectFace recognitiones_PE
dc.subjectEar recognitiones_PE
dc.subjectFeature-level fusiónes_PE
dc.subjectScore-level fusiónes_PE
dc.subjectTwo-stream CNN.es_PE
dc.subject.ocdehttp://purl.org/pe-repo/ocde/ford#1.02.01
dc.titleMultimodal unconstrained people recognition with face and ear images using deep learninges_PE
dc.typeinfo:eu-repo/semantics/masterThesis
dc.type.versioninfo:eu-repo/semantics/publishedVersion
renati.advisor.dni30960286
renati.advisor.orcidhttps://orcid.org/0000-0003-2440-0247
renati.author.dni47198912
renati.discipline611017
renati.jurorOchoa Luna, José Eduardo
renati.jurorMora Colque, Rensso Victor Hugo
renati.jurorCayllahua Cahuina, Edward Jorge Yuri
renati.jurorMenotti, David
renati.levelhttps://purl.org/pe-repo/renati/level#maestro
renati.typehttps://purl.org/pe-repo/renati/type#tesis
thesis.degree.disciplineCiencia de la Computaciónes_PE
thesis.degree.grantorUniversidad Católica San Pablo. Departamento de Ciencia de la Computación
thesis.degree.levelMaestríaes_PE
thesis.degree.nameMaestro en Ciencia de la Computaciónes_PE
thesis.degree.programEscuela Profesional Ciencia de la Computaciónes_PE
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