In this work we test the ability of deep learning methods to provide an
end-to-end mapping between low and high resolution images applying it to the
iris recognition problem. Here, we propose the use of two deep learning
single-image super-resolution approaches: Stacked Auto-Encoders (SAE) and
Convolutional Neural Networks (CNN) with the most possible lightweight
structure to achieve fast speed, preserve local information and reduce
artifacts at the same time. We validate the methods with a database of 1.872
near-infrared iris images with quality assessment and recognition experiments
showing the superiority of deep learning approaches over the compared
algorithms.Comment: Published at Proc. 25th European Signal Processing Conference,
EUSIPCO 201