@InProceedings{LucioLarZanMorMen:2019:SiIrPe,
               author = "Lucio, Diego Rafael and Laroca, Rayson and Zanlorensi, Luiz 
                         Antonio and Moreira, Gladston and Menotti, David",
          affiliation = "{Federal University of Paran{\'a}} and {Federal University of 
                         Paran{\'a}} and {Federal University of Paran{\'a}} and {Federal 
                         University of Ouro Preto} and {Federal University of 
                         Paran{\'a}}",
                title = "Simultaneous Iris and Periocular Region Detection Using Coarse 
                         Annotations",
            booktitle = "Proceedings...",
                 year = "2019",
               editor = "Oliveira, Luciano Rebou{\c{c}}as de and Sarder, Pinaki and Lage, 
                         Marcos and Sadlo, Filip",
         organization = "Conference on Graphics, Patterns and Images, 32. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "iris, periocular, detection, simultaneous.",
             abstract = "In this work, we propose to detect the iris and periocular regions 
                         simultaneously using coarse annotations and two well-known object 
                         detectors: YOLOv2 and Faster R-CNN. We believe coarse annotations 
                         can be used in recognition systems based on the iris and 
                         periocular regions, given the much smaller engineering effort 
                         required to manually annotate the training images. We manually 
                         made coarse annotations of the iris and periocular regions (~ 122K 
                         images from the visible (VIS) spectrum and ~ 38K images from the 
                         near-infrared (NIR) spectrum). The iris annotations in the NIR 
                         databases were generated semi-automatically by first applying an 
                         iris segmentation CNN and then performing a manual inspection. 
                         These annotations were made for 11 well-known public databases (3 
                         NIR and 8 VIS) designed for the iris-based recognition problem, 
                         and are publicly available to the research community1. 
                         Experimenting our proposal on these databases, we highlight two 
                         results. First, the Faster R-CNN + Feature Pyramid Network (FPN) 
                         model reported an Intersection over Union (IoU) higher than YOLOv2 
                         (91.86% vs 85.30%). Second, the detection of the iris and 
                         periocular regions being performed simultaneously is as accurate 
                         as performed separately, but with a lower computational cost, i.e. 
                         two tasks were carried out at the cost of one.",
  conference-location = "Rio de Janeiro, RJ, Brazil",
      conference-year = "28-31 Oct. 2019",
                  doi = "10.1109/SIBGRAPI.2019.00032",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2019.00032",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/3U56U6E",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/3U56U6E",
           targetfile = "2019_SIBGRAPI_IRIS_PRD_Detection.pdf",
        urlaccessdate = "2024, Apr. 28"
}