  Iris recognition has been an active research area during last few decades,
because of its wide applications in security, from airports to homeland
security border control. Different features and algorithms have been proposed
for iris recognition in the past. In this paper, we propose an end-to-end deep
learning framework for iris recognition based on residual convolutional neural
network (CNN), which can jointly learn the feature representation and perform
recognition. We train our model on a well-known iris recognition dataset using
only a few training images from each class, and show promising results and
improvements over previous approaches. We also present a visualization
technique which is able to detect the important areas in iris images which can
mostly impact the recognition results. We believe this framework can be widely
used for other biometrics recognition tasks, helping to have a more scalable
and accurate systems.
