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http://www.repositorio.ufop.br/jspui/handle/123456789/10370
Título: | Deep periocular representation aiming video surveillance. |
Autor(es): | Moreira, Gladston Juliano Prates Luz, Eduardo José da Silva Zanlorensi Junior, Luiz Antonio Gomes, David Menotti |
Palavras-chave: | Deep learning Transfer learning VGG Periocular region Video surveillance |
Data do documento: | 2017 |
Referência: | MOREIRA, 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. |
Resumo: | Usually, 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. |
URI: | http://www.repositorio.ufop.br/handle/123456789/10370 |
Link para o artigo: | https://www.sciencedirect.com/science/article/pii/S0167865517304476 |
ISSN: | 01678655 |
Aparece nas coleções: | DECOM - Artigos publicados em periódicos |
Arquivos associados a este item:
Arquivo | Descrição | Tamanho | Formato | |
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ARTIGO_DeepPeriocularRepresentation.pdf Restricted Access | 2,44 MB | Adobe PDF | Visualizar/Abrir |
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