Exploring Deep Learning Image Super-Resolution for Iris Recognition
Eduardo Ribeiro Andreas Uhl Fernando Alonso-Fernandez Reuben A. Farrugia
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.