@InProceedings{BezerraLaLuSeOlBrMe:2018:RoIrSe,
               author = "Bezerra, Cides and Laroca, Rayson and Lucio, Diego R. and Severo, 
                         Evair and Oliveira, Lucas F. and Britto Jr, Alceu S. and Menotti, 
                         David",
          affiliation = "{Federal University of Parana} and {Federal University of Parana} 
                         and {Federal University of Parana} and {Federal University of 
                         Parana} and {Federal University of Parana} and {Pontifical 
                         Catholic University of Parana} and {Federal University of 
                         Parana}",
                title = "Robust Iris Segmentation Based on Fully Convolutional Networks and 
                         Generative Adversarial Networks",
            booktitle = "Proceedings...",
                 year = "2018",
               editor = "Ross, Arun and Gastal, Eduardo S. L. and Jorge, Joaquim A. and 
                         Queiroz, Ricardo L. de and Minetto, Rodrigo and Sarkar, Sudeep and 
                         Papa, Jo{\~a}o Paulo and Oliveira, Manuel M. and Arbel{\'a}ez, 
                         Pablo and Mery, Domingo and Oliveira, Maria Cristina Ferreira de 
                         and Spina, Thiago Vallin and Mendes, Caroline Mazetto and Costa, 
                         Henrique S{\'e}rgio Gutierrez and Mejail, Marta Estela and Geus, 
                         Klaus de and Scheer, Sergio",
         organization = "Conference on Graphics, Patterns and Images, 31. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Biometric, Iris segmentation, Non-cooperative.",
             abstract = "The iris can be considered as one of the most important biometric 
                         traits due to its high degree of uniqueness. Iris-based biometrics 
                         applications depend mainly on the iris segmentation whose 
                         suitability is not robust for different environments such as 
                         near-infrared (NIR) and visible (VIS) ones. In this paper, two 
                         approaches for robust iris segmentation based on Fully 
                         Convolutional Networks (FCNs) and Generative Adversarial Networks 
                         (GANs) are described. Similar to a common convolutional network, 
                         but without the fully connected layers (i.e., the classification 
                         layers), an FCN employs at its end a combination of pooling layers 
                         from different convolutional layers. Based on the game theory, a 
                         GAN is designed as two networks competing with each other to 
                         generate the best segmentation. The proposed segmentation networks 
                         achieved promising results in all evaluated datasets (i.e., 
                         BioSec, CasiaI3, CasiaT4, IITD-1) of NIR images and (NICE.I, 
                         CrEye-Iris and MICHE-I) of VIS images in both non-cooperative and 
                         cooperative domains, outperforming the baselines techniques which 
                         are the best ones found so far in the literature, i.e., a new 
                         state of the art for these datasets. Furthermore, we manually 
                         labeled 2,431 images from CasiaT4, CrEye-Iris and MICHE-I 
                         datasets, making the masks available for research purposes.",
  conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
      conference-year = "29 Oct.-1 Nov. 2018",
                  doi = "10.1109/SIBGRAPI.2018.00043",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2018.00043",
             language = "en",
                  ibi = "8JMKD3MGPAW/3RPB5DL",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3RPB5DL",
           targetfile = "2018_SIBGRAPI_IrisSeg.pdf",
        urlaccessdate = "2024, Apr. 27"
}