  The use of low-resolution images adopting more relaxed acquisition conditions
such as mobile phones and surveillance videos is becoming increasingly common
in iris recognition nowadays. Concurrently, a great variety of single image
super-resolution techniques are emerging, especially with the use of
convolutional neural networks (CNNs). The main objective of these methods is to
try to recover finer texture details generating more photo-realistic images
based on the optimisation of an objective function depending basically on the
CNN architecture and training approach. In this work, the authors explore
single image super-resolution using CNNs for iris recognition. For this, they
test different CNN architectures and use different training databases,
validating their approach on a database of 1.872 near infrared iris images and
on a mobile phone image database. They also use quality assessment, visual
results and recognition experiments to verify if the photo-realism provided by
the CNNs which have already proven to be effective for natural images can
reflect in a better recognition rate for iris recognition. The results show
that using deeper architectures trained with texture databases that provide a
balance between edge preservation and the smoothness of the method can lead to
good results in the iris recognition process.
