  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.
