author = "Lima, Diego Filipe Souza de and Batista, Leonardo Vidal",
          affiliation = "{Federal University of Para{\'{\i}}ba} and {Federal University 
                         of Para{\'{\i}}ba}",
                title = "Segmenta{\c{c}}{\~a}o de Imagens de {\'{\I}}ris Utilizando 
                         Deep Learning",
            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 = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "{\'{\I}}ris, Segmenta{\c{c}}{\~a}o, Deep Learning, 
             abstract = "Current biometric systems can recognize individuals through 
                         various trait such as fingerprint, face, iris, palm, etc. Among 
                         these varied characteristics, the iris is one that most needs the 
                         collaboration of the individual. On the other hand, it is one of 
                         the most reliable forms of recognition because of the unique 
                         patterns it has in its composition. However, the use of this trait 
                         in a non-cooperative way means that the current systems perform 
                         below satisfactory, mainly due to the difficulty of locating and 
                         segmenting the iris region, which generates errors that are 
                         propagated throughout the recognition process, affecting the final 
                         performance of the systems directly. The present work proposes an 
                         iris segmentation algorithm using a Deep Learning technique known 
                         as Convolutional Autoencoder, which can perform satisfactorily in 
                         both cooperative and non-cooperative environments. The 
                         satisfactory performance of the algorithm was evident when 
                         compared to algorithms present in the literature, using images 
                         with similar capture patterns. The results of the segmentation 
                         process were evaluated using iris segmentation error and 
                         computational vision metrics, then compared with some of the best 
                         results found in the literature. The proposed method achieved in 
                         some cases an error rate 68% lower than the other algorithms.",
  conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
      conference-year = "Oct. 29 - Nov. 1, 2018",
             language = "pt",
                  ibi = "8JMKD3MGPAW/3S4EE6B",
                  url = "",
           targetfile = "Segmenta{\c{c}}{\~a}o de Imagens de {\'{\I}}ris Utilizando 
                         Deep Learning.pdf",
        urlaccessdate = "2021, June 18"