  Several recent works have addressed the ability of deep learning to disclose
rich, hierarchical and discriminative models for the most diverse purposes.
Specifically in the super-resolution field, Convolutional Neural Networks
(CNNs) using different deep learning approaches attempt to recover realistic
texture and fine grained details from low resolution images. In this work we
explore the viability of these approaches for iris Super-Resolution (SR) in an
iris recognition environment. For this, we test different architectures with
and without a so called image re-projection to reduce artifacts applying it to
different iris databases to verify the viability of the different CNNs for iris
super-resolution. Results show that CNNs and image re-projection can improve
the results specially for the accuracy of recognition systems using a complete
different training database performing the transfer learning successfully.
