@InProceedings{SilvaLuBaPeFaMe:2015:ApIrCo,
               author = "Silva, Pedro and Luz, Eduardo and Baeta, Rafael and Pedrini, Helio 
                         and Falcao, Alexandre Xavier and Menotti, David",
          affiliation = "{Federal University of Ouro Preto} and {Federal University of Ouro 
                         Preto} and {Federal University of Ouro Preto} and {University of 
                         Campinas} and {University of Campinas} and {Federal University of 
                         Ouro Preto}",
                title = "An Approach to Iris Contact Lens Detection based on Deep Image 
                         Representations",
            booktitle = "Proceedings...",
                 year = "2015",
               editor = "Papa, Jo{\~a}o Paulo and Sander, Pedro Vieira and Marroquim, 
                         Ricardo Guerra and Farrell, Ryan",
         organization = "Conference on Graphics, Patterns and Images, 28. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "biometrics, contact lens detection, deep learning, convolutional 
                         networks.",
             abstract = "Spoofing detection is a challenging task in biometric systems, 
                         when differentiating illegitimate users from genuine ones. 
                         Although iris scans are far more inclusive than fingerprints, and 
                         also more precise for person authentication, iris recognition 
                         systems are vulnerable to spoofing via textured cosmetic contact 
                         lenses. Iris spoofing detection is also referred to as liveness 
                         detection (binary classification of fake and real images). In this 
                         work, we focus on a three-class detection problem: images with 
                         textured (colored) contact lenses, soft contact lenses, and no 
                         lenses. Our approach uses a convolutional network to build a deep 
                         image representation and an additional fully-connected single 
                         layer with softmax regression for classification. Experiments are 
                         conducted in comparison with a state-of-the-art approach (SOTA) on 
                         two public iris image databases for contact lens detection: 2013 
                         Notre Dame and IIIT-Delhi. Our approach can achieve a 30% 
                         performance gain over SOTA on the former database (from 80% to 
                         86%) and comparable results on the latter. Since IIIT-Delhi does 
                         not provide segmented iris images and, differently from SOTA, our 
                         approach does not segment the iris yet, we conclude that these are 
                         very promising results.",
  conference-location = "Salvador, BA, Brazil",
      conference-year = "26-29 Aug. 2015",
                  doi = "10.1109/SIBGRAPI.2015.16",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2015.16",
             language = "en",
                  ibi = "8JMKD3MGPBW34M/3JLT3D2",
                  url = "http://urlib.net/ibi/8JMKD3MGPBW34M/3JLT3D2",
           targetfile = "PID3758179.pdf",
        urlaccessdate = "2024, Apr. 28"
}