Deep periocular representation aiming video surveillance.

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dc.contributor.authorMoreira, Gladston Juliano Prates
dc.contributor.authorLuz, Eduardo José da Silva
dc.contributor.authorZanlorensi Junior, Luiz Antonio
dc.contributor.authorGomes, David Menotti
dc.date.accessioned2018-10-16T12:55:19Z
dc.date.available2018-10-16T12:55:19Z
dc.date.issued2017
dc.description.abstractUsually, 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.pt_BR
dc.identifier.citationMOREIRA, G. J. P. et al. Deep periocular representation aiming video surveillance. Pattern Recognition Letters, v. 114, p. 2-12, 2018. Disponível em: <https://www.sciencedirect.com/science/article/pii/S0167865517304476>. Acesso em: 16 jun. 2018.pt_BR
dc.identifier.issn 01678655
dc.identifier.urihttp://www.repositorio.ufop.br/handle/123456789/10370
dc.identifier.uri2https://www.sciencedirect.com/science/article/pii/S0167865517304476pt_BR
dc.language.isoen_USpt_BR
dc.rightsabertopt_BR
dc.subjectDeep learningpt_BR
dc.subjectTransfer learningpt_BR
dc.subjectVGG Periocular regionpt_BR
dc.subjectVideo surveillancept_BR
dc.titleDeep periocular representation aiming video surveillance.pt_BR
dc.typeDissertacaopt_BR
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