  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 community. 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.
