Publications:Iris Super-Resolution Using Iterative Neighbor Embedding


Do not edit this section

Keep all hand-made modifications below

Title Iris Super-Resolution Using Iterative Neighbor Embedding
Author Fernando Alonso-Fernandez and Reuben A. Farrugia and Josef Bigun
Year 2017
PublicationType Conference Paper
Conference International Conference on Computer Vision and Pattern Recognition, CVPR, IEEE Computer Society Workshop on Biometrics, Hawaii Convention Center HI, USA, 21-26 Jul, 2017
Diva url
Abstract Iris recognition research is heading towards enabling more relaxed acquisition conditions. This has effects on the quality and resolution of acquired images, severely affecting the accuracy of recognition systems if not tackled appropriately. In this paper, we evaluate a super-resolution algorithm used to reconstruct iris images based on iterative neighbor embedding of local image patches which tries to represent input low-resolution patches while preserving the geometry of the original high-resolution space. To this end, the geometry of the low- and high-resolution manifolds are jointly considered during the reconstruction process. We validate the system with a database of 1,872 near-infrared iris images, while fusion of two iris comparators has been adopted to improve recognition performance. The presented approach is substantially superior to bilinear/bicubic interpolations at very low resolutions, and it also outperforms a previous PCA-based iris reconstruction approach which only considers the geometry of the low-resolution manifold during the reconstruction process.