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A Survey of Super-Resolution in Iris Biometrics with Evaluation of Dictionary-Learning
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-1400-346X
University of Malta, Msida, Malta.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-4929-1262
Universidad Autonoma de Madrid, Madrid, Spain.
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2019 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 6519-6544Article in journal (Refereed) Published
Abstract [en]

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, an overall 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 performance. 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 Eigentransformation 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 15 × 15 pixels being the smallest resolution evaluated. To the best of our knowledge, this is among the smallest resolutions employed in the literature. The experimental framework is complemented with six publicly available 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 attain an impressive Equal Error Rate as low as 5%, and a Top-1 accuracy of 77-84% when considering iris images of only 15 × 15 pixels. These results clearly demonstrate the benefit of using trained super-resolution techniques to improve the quality of iris images prior to matching. © 2018, Emerald Publishing Limited.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2019. Vol. 7, p. 6519-6544
Keywords [en]
Iris hallucination, iris recognition, eigen-patch, super-resolution, PCA
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-38659DOI: 10.1109/ACCESS.2018.2889395Scopus ID: 2-s2.0-85059007584OAI: oai:DiVA.org:hh-38659DiVA, id: diva2:1273284
Funder
Swedish Research Council, 2016-03497EU, FP7, Seventh Framework Programme, COST IC1106Knowledge Foundation, SIDUS-AIRKnowledge Foundation, CAISRVINNOVA, 2018-00472Available from: 2018-12-20 Created: 2018-12-20 Last updated: 2019-01-25Bibliographically approved

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Alonso-Fernandez, FernandoBigun, Josef

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