Ocular Recognition Using Deep Features for Identity Authentication

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.creatorVizoni, Marcelo V. [UNESP]-
Autor(es): dc.creatorMarana, Aparecido N. [UNESP]-
Autor(es): dc.creatorPaiva, A. C.-
Autor(es): dc.creatorConci, A.-
Autor(es): dc.creatorBraz, G.-
Autor(es): dc.creatorAlmeida, JDS-
Autor(es): dc.creatorFernandes, LAF-
Data de aceite: dc.date.accessioned2022-02-22T00:55:38Z-
Data de disponibilização: dc.date.available2022-02-22T00:55:38Z-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2019-12-31-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/209188-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/209188-
Descrição: dc.descriptionRecently, ocular biometrics has been gaining importance in Biometrics due to the poor performance obtained in some cases by biometric systems based on characteristics of the whole face. This paper presents a new method for person authentication based on ocular deep features, which are extracted from the ocular region of the face by using a very deep CNN (Convolutional Neural Network). Another interesting aspect of our method is that, instead of using directly the deep features as input for the authentication system, it uses the difference between the probe and gallery deep features. So, our method adopts a pairwise strategy. A binary support vector machine is trained to determine whether a given difference vector is genuine or impostor. The proposed new method based on such pairwise strategy was evaluated using the ocular left set of the UBIPr dataset and five pre-trained CNN architectures. When using the pre-trained VGG-Face the proposed method obtained a state-of-the-art result (3.18% of Equal Error Rate).-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionNVIDIA Corporation (GPU Grant Program)-
Descrição: dc.descriptionSao Paulo State Univ UNESP, Bauru, SP, Brazil-
Descrição: dc.descriptionSao Paulo State Univ UNESP, Bauru, SP, Brazil-
Descrição: dc.descriptionCAPES: 001-
Formato: dc.format155-160-
Idioma: dc.languageen-
Publicador: dc.publisherIeee-
Relação: dc.relationProceedings Of The 2020 International Conference On Systems, Signals And Image Processing (iwssip), 27th Edition-
???dc.source???: dc.sourceWeb of Science-
Palavras-chave: dc.subjectocular biometrics-
Palavras-chave: dc.subjectdeep learning-
Palavras-chave: dc.subjectconvolutional neural networks-
Palavras-chave: dc.subjectperson authentication-
Título: dc.titleOcular Recognition Using Deep Features for Identity Authentication-
Aparece nas coleções:Repositório Institucional - Unesp

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