  This paper offers three new, open-source, deep learning-based iris
segmentation methods, and the methodology how to use irregular segmentation
masks in a conventional Gabor-wavelet-based iris recognition. To train and
validate the methods, we used a wide spectrum of iris images acquired by
different teams and different sensors and offered publicly, including data
taken from CASIA-Iris-Interval-v4, BioSec, ND-Iris-0405, UBIRIS,
Warsaw-BioBase-Post-Mortem-Iris v2.0 (post-mortem iris images), and
ND-TWINS-2009-2010 (iris images acquired from identical twins). This varied
training data should increase the generalization capabilities of the proposed
segmentation techniques. In database-disjoint training and testing, we show
that deep learning-based segmentation outperforms the conventional (OSIRIS)
segmentation in terms of Intersection over Union calculated between the
obtained results and manually annotated ground-truth. Interestingly, the
Gabor-based iris matching is not always better when deep learning-based
segmentation is used, and is on par with the method employing Daugman's based
segmentation.
