Paper Info
Title
Eigen-patch iris super-resolution for iris recognition improvement
Abstract
Low image resolution will be a predominant factor in iris recognition systems as they evolve towards more relaxed acquisition conditions. Here, we propose a super-resolution technique to enhance iris images based on Principal Component Analysis (PCA) Eigen-transformation of local image patches. Each patch is reconstructed separately, allowing better quality of enhanced images by preserving local information and reducing artifacts. We validate the system used a database of 1,872 near-infrared iris images. Results show the superiority of the presented approach over bilinear or bicubic interpolation, with the eigen-patch method being more resilient to image resolution reduction. We also perform recognition experiments with an iris matcher based 1D Log-Gabor, demonstrating that verification rates degrades more rapidly with bilinear or bicubic interpolation.
Year
Venue
Keywords
2015
European Signal Processing Conference
Iris hallucination,iris recognition,eigen-patch,super-resolution,Principal Component Analysis
Field
DocType
ISSN
Iterative reconstruction,Signal processing,Iris recognition,Computer vision,Pattern recognition,Computer science,Bicubic interpolation,Artificial intelligence,Image resolution,Superresolution,Principal component analysis,Bilinear interpolation
Conference
2076-1465
Citations 
PageRank 
References 
5
0.45
7
Authors
3
Name
Order
Citations
PageRank
Fernando Alonso-Fernandez153137.65
Reuben A. Farrugia211118.26
Josef Bigün3876194.07