  Iris segmentation and localization in unconstrained environments is
challenging due to long distances, illumination variations, limited user
cooperation, and moving subjects. To address this problem, we present a U-Net
with a pre-trained MobileNetV2 deep neural network method. We employ the
pre-trained weights given with MobileNetV2 for the ImageNet dataset and
fine-tune it on the iris recognition and localization domain. Further, we have
introduced a new dataset, called KartalOl, to better evaluate detectors in iris
recognition scenarios. To provide domain adaptation, we fine-tune the
MobileNetV2 model on the provided data for NIR-ISL 2021 from the
CASIA-Iris-Asia, CASIA-Iris-M1, and CASIA-Iris-Africa and our dataset. We also
augment the data by performing left-right flips, rotation, zoom, and
brightness. We chose the binarization threshold for the binary masks by
iterating over the images in the provided dataset. The proposed method is
tested and trained in CASIA-Iris-Asia, CASIA-Iris-M1, CASIA-Iris-Africa, along
the KartalOl dataset. The experimental results highlight that our method
surpasses state-of-the-art methods on mobile-based benchmarks. The codes and
evaluation results are publicly available at
https://github.com/Jalilnkh/KartalOl-NIR-ISL2021031301.
