Simultaneous Iris and Periocular Region Detection Using Coarse Annotations - Lúcio, Diego R.; Laroca, Rayson; Zanlorensi, Luiz A.; Menotti, David; Moreira, Gladston (Anais da Conference on Graphics, Patterns and Images (SIBGRAPI); 2019: Anais da XXXII Conference on Graphics, Patterns and Images, 2019)
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 the periocular region, given the much smaller engineering effort required to manually annotate the training images. We manually made coarse annotations of the iris and periocular regions (â‰ˆ 122K images from visible (VIS)spectrum and â‰ˆ 38K images from near-infrared (NIR) spectrum). The iris annotations in the NIR databases were generated semi-automatically, applying an iris segmentation CNN, and thenperforming a manual correction. 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 availableto the research community. Experimenting our proposal on these databases, we highlight two results. First, the Faster R-CNN + Feature Pyramid Network (FPN) model reported an Intersectionover Union (IoU) higher than the YoloV2 model (91.86% vs 85.30%). Second, the detection of the iris and periocular regions being performed simultaneously is as accurate as performedseparately, with lower computational cost, i.e., two tasks were carried out at the cost of one.