  With the increasing imaging and processing capabilities of today's mobile
devices, user authentication using iris biometrics has become feasible.
However, as the acquisition conditions become more unconstrained and as image
quality is typically lower than dedicated iris acquisition systems, the
accurate segmentation of iris regions is crucial for these devices. In this
work, an end to end Fully Convolutional Deep Neural Network (FCDNN) design is
proposed to perform the iris segmentation task for lower-quality iris images.
The network design process is explained in detail, and the resulting network is
trained and tuned using several large public iris datasets. A set of methods to
generate and augment suitable lower quality iris images from the high-quality
public databases are provided. The network is trained on Near InfraRed (NIR)
images initially and later tuned on additional datasets derived from visible
images. Comprehensive inter-database comparisons are provided together with
results from a selection of experiments detailing the effects of different
tunings of the network. Finally, the proposed model is compared with
SegNet-basic, and a near-optimal tuning of the network is compared to a
selection of other state-of-art iris segmentation algorithms. The results show
very promising performance from the optimized Deep Neural Networks design when
compared with state-of-art techniques applied to the same lower quality
datasets.
