Deep periocular representation aiming video surveillance.

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.creatorMoreira, Gladston Juliano Prates-
Autor(es): dc.creatorLuz, Eduardo José da Silva-
Autor(es): dc.creatorZanlorensi Junior, Luiz Antonio-
Autor(es): dc.creatorGomes, David Menotti-
Data de aceite: dc.date.accessioned2025-08-21T15:03:53Z-
Data de disponibilização: dc.date.available2025-08-21T15:03:53Z-
Data de envio: dc.date.issued2018-10-16-
Data de envio: dc.date.issued2018-10-16-
Data de envio: dc.date.issued2017-
Fonte completa do material: dc.identifierhttp://www.repositorio.ufop.br/handle/123456789/10370-
Fonte completa do material: dc.identifierhttps://www.sciencedirect.com/science/article/pii/S0167865517304476-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1001788-
Descrição: dc.descriptionUsually, in the deep learning community, it is claimed that generalized representations that yielding out- standing performance / effectiveness require a huge amount of data for learning, which directly affect biometric applications. However, recent works combining transfer learning from other domains have sur- mounted such data application constraints designing interesting and promising deep learning approaches in diverse scenarios where data is not so abundant. In this direction, a biometric system for the peri- ocular region based on deep learning approach is designed and applied on two non-cooperative ocular databases. Impressive representation discrimination is achieved with transfer learning from the facial do- main (a deep convolutional network, called VGG) and fine tuning in the specific periocular region domain. With this design, our proposal surmounts previous state-of-the-art results on NICE (mean decidability of 3.47 against 2.57) and MobBio (equal error rate of 5.42% against 8.73%) competition databases.-
Formato: dc.formatapplication/pdf-
Idioma: dc.languageen-
Direitos: dc.rightsaberto-
Palavras-chave: dc.subjectDeep learning-
Palavras-chave: dc.subjectTransfer learning-
Palavras-chave: dc.subjectVGG Periocular region-
Palavras-chave: dc.subjectVideo surveillance-
Título: dc.titleDeep periocular representation aiming video surveillance.-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - UFOP

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