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Camara Chavez, Guillermo
Ramos Cooper, Solange Griselly
2023-11-15T16:25:34Z
2023-11-15T16:25:34Z
2023
1080188
https://hdl.handle.net/20.500.12590/17819
Multibiometric 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.
Tesis de maestría
application/pdf
eng
Universidad Católica San pablo
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc/4.0/
Multibiometric system
Multimodal recognition
Face recognition
Ear recognition
Feature-level fusión
Score-level fusión
Two-stream CNN.
Multimodal unconstrained people recognition with face and ear images using deep learning
info:eu-repo/semantics/masterThesis
Universidad Católica San Pablo. Departamento de Ciencia de la Computación
Ciencia de la Computación
Maestría
Maestro en Ciencia de la Computación
PE
http://purl.org/pe-repo/ocde/ford#1.02.01
Escuela Profesional Ciencia de la Computación
https://purl.org/pe-repo/renati/level#maestro
30960286
https://orcid.org/0000-0003-2440-0247
47198912
611017
Ochoa Luna, José Eduardo
Mora Colque, Rensso Victor Hugo
Cayllahua Cahuina, Edward Jorge Yuri
Menotti, David
https://purl.org/pe-repo/renati/type#tesis
Privada asociativa
info:eu-repo/semantics/publishedVersion



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