  The use of iris as a biometric trait is widely used because of its high level
of distinction and uniqueness. Nowadays, one of the major research challenges
relies on the recognition of iris images obtained in visible spectrum under
unconstrained environments. In this scenario, the acquired iris are affected by
capture distance, rotation, blur, motion blur, low contrast and specular
reflection, creating noises that disturb the iris recognition systems. Besides
delineating the iris region, usually preprocessing techniques such as
normalization and segmentation of noisy iris images are employed to minimize
these problems. But these techniques inevitably run into some errors. In this
context, we propose the use of deep representations, more specifically,
architectures based on VGG and ResNet-50 networks, for dealing with the images
using (and not) iris segmentation and normalization. We use transfer learning
from the face domain and also propose a specific data augmentation technique
for iris images. Our results show that the approach using non-normalized and
only circle-delimited iris images reaches a new state of the art in the
official protocol of the NICE.II competition, a subset of the UBIRIS database,
one of the most challenging databases on unconstrained environments, reporting
an average Equal Error Rate (EER) of 13.98% which represents an absolute
reduction of about 5%.
