A Survey of Super-Resolution in Iris Biometrics with Evaluation of Dictionary-Learning

TL;DR

This paper surveys iris super-resolution methods, introduces a local patch-based reconstruction technique, and demonstrates significant improvements in recognition accuracy on very low-resolution iris images.

Contribution

It presents a novel patch-based eigen-reconstruction method for iris super-resolution and evaluates its effectiveness on extremely low-resolution images, outperforming traditional interpolation methods.

Findings

Significant reduction in EER to 5% at 15x15 resolution

Improved Top-1 accuracy to 77-84% with the proposed method

Outperforms bilinear and bicubic interpolation in low-resolution iris recognition

Abstract

The lack of resolution has a negative impact on the performance of image-based biometrics. While many generic super-resolution methods have been proposed to restore low-resolution images, they usually aim to enhance their visual appearance. However, a visual enhancement of biometric images does not necessarily correlate with a better recognition performance. Reconstruction approaches need thus to incorporate specific information from the target biometric modality to effectively improve recognition. This paper presents a comprehensive survey of iris super-resolution approaches proposed in the literature. We have also adapted an Eigen-patches reconstruction method based on PCA Eigen-transformation of local image patches. The structure of the iris is exploited by building a patch-position dependent dictionary. In addition, image patches are restored separately, having their own reconstruction weights. This allows the solution to be locally optimized, helping to preserve local information. To evaluate the algorithm, we degraded high-resolution images from the CASIA Interval V3 database. Different restorations were considered, with 15x15 pixels being the smallest resolution. To the best of our knowledge, this is among the smallest resolutions employed in the literature. The framework is complemented with six public iris comparators, which were used to carry out biometric verification and identification experiments. Experimental results show that the proposed method significantly outperforms both bilinear and bicubic interpolation at very low-resolution. The performance of a number of comparators attains an impressive Equal Error Rate as low as 5%, and a Top-1 accuracy of 77-84% when considering iris images of only 15x15 pixels. These results clearly demonstrate the benefit of using trained super-resolution techniques to improve the quality of iris images prior to matching.