Browsing by Author "Zanlorensi Junior, Luiz Antonio"

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Deep periocular representation aiming video surveillance.
    (2017) Moreira, Gladston Juliano Prates; Luz, Eduardo José da Silva; Zanlorensi Junior, Luiz Antonio; Gomes, David Menotti
    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.
  • No Thumbnail Available
    Item
    Ocular recognition databases and competitions : a survey.
    (2022) Zanlorensi Junior, Luiz Antonio; Laroca, Rayson; Luz, Eduardo José da Silva; Britto Junior, Alceu de Souza; Oliveira, Luiz Eduardo Soares de; Menotti, David
    The use of the iris and periocular region as biometric traits has been extensively investigated, mainly due to the singularity of the iris features and the use of the periocular region when the image resolution is not sufcient to extract iris information. In addition to providing information about an individual’s identity, features extracted from these traits can also be explored to obtain other information such as the individual’s gender, the infuence of drug use, the use of contact lenses, spoofng, among others. This work presents a survey of the databases created for ocular recognition, detailing their protocols and how their images were acquired. We also describe and discuss the most popular ocular recognition competitions (contests), highlighting the submitted algorithms that achieved the best results using only iris trait and also fusing iris and periocular region information. Finally, we describe some relevant works applying deep learning techniques to ocular recognition and point out new challenges and future directions. Considering that there are a large number of ocular databases, and each one is usually designed for a specifc problem, we believe this survey can provide a broad overview of the challenges in ocular biometrics.