Exploring Deep Learning Image Super-Resolution for Iris Recognition
This work addresses the domain-specific problem of enhancing iris recognition accuracy for biometric systems, presenting an incremental improvement by applying established deep learning methods to this task.
The paper tackled the problem of improving iris recognition by using deep learning for single-image super-resolution, achieving superior performance over existing algorithms in quality assessment and recognition experiments on a dataset of 1,872 near-infrared iris images.
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.