Deep periocular representation aiming video surveillance.
| Autor(a) principal: | |
|---|---|
| Data de Publicação: | 2017 |
| Outros Autores: | , , |
| Tipo de documento: | Dissertação |
| Idioma: | eng |
| Título da fonte: | Repositório Institucional da UFOP |
| Texto Completo: | http://www.repositorio.ufop.br/handle/123456789/10370 https://www.sciencedirect.com/science/article/pii/S0167865517304476 |
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. |