Exploring Deep Learning Image Super-Resolution for Iris Recognition

Abstract

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.

Published at Proc. 25th European Signal Processing Conference, EUSIPCO 2017

References in corpus (1)

• Iris Recognition Based on SIFT Features

Cited by in corpus (5)

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• A Survey of Super-Resolution in Iris Biometrics with Evaluation of Dictionary-Learning
• Super-Resolution and Image Re-projection for Iris Recognition
• Generative Iris Prior Embedded Transformer for Iris Restoration
• Deep GAN-Based Cross-Spectral Cross-Resolution Iris Recognition