Publications:Learning-Based Local-Patch Resolution Reconstruction of Iris Smartphone Images

From ISLAB/CAISR
Revision as of 21:22, 8 August 2017 by Slawek (Talk | contribs)

(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)

Do not edit this section

Keep all hand-made modifications below

Title Learning-Based Local-Patch Resolution Reconstruction of Iris Smartphone Images
Author Fernando Alonso-Fernandez and Reuben Farrugia and Josef Bigun
Year 2017
PublicationType Conference Paper
Journal
HostPublication
Conference IEEE/IAPR International Joint Conference on Biometrics, IJCB, Denver, Colorado, USA, October 1-4, 2017
DOI
Diva url http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:1129962
Abstract Application of ocular biometrics in mobile and at a distance environments still has several open challenges, with the lack quality and resolution being an evident issue that can severely affects performance. In this paper, we evaluate two trained image reconstruction algorithms in the context of smart-phone biometrics. They are based on the use of coupled dictionaries to learn the mapping relations between low and high resolution images. In addition, reconstruction is made in local overlapped image patches, where up-scaling functions are modelled separately for each patch, allowing to better preserve local details. The experimental setup is complemented with a database of 560 images captured with two different smart-phones, and two iris comparators employed for verification experiments. We show that the trained approaches are substantially superior to bilinear or bicubic interpolations at very low resolutions (images of 13×13 pixels). Under such challenging conditions, an EER of ∼7% can be achieved using individual comparators, which is further pushed down to 4-6% after the fusion of the two systems.