Super-Resolution and Image Re-projection for Iris Recognition

TL;DR

This paper investigates the use of CNN-based super-resolution and image re-projection techniques to enhance iris recognition accuracy from low-resolution images, demonstrating improved recognition performance across different databases.

Contribution

It explores the effectiveness of CNN architectures combined with image re-projection for iris super-resolution, showing successful transfer learning across databases.

Findings

CNNs improve iris recognition accuracy from low-resolution images

Image re-projection reduces artifacts and enhances detail

Transfer learning enables effective application across different datasets

Abstract

Several recent works have addressed the ability of deep learning to disclose rich, hierarchical and discriminative models for the most diverse purposes. Specifically in the super-resolution field, Convolutional Neural Networks (CNNs) using different deep learning approaches attempt to recover realistic texture and fine grained details from low resolution images. In this work we explore the viability of these approaches for iris Super-Resolution (SR) in an iris recognition environment. For this, we test different architectures with and without a so called image re-projection to reduce artifacts applying it to different iris databases to verify the viability of the different CNNs for iris super-resolution. Results show that CNNs and image re-projection can improve the results specially for the accuracy of recognition systems using a complete different training database performing the transfer learning successfully.