Paper Info
Title
Very low-resolution iris recognition via Eigen-patch super-resolution and matcher fusion
Abstract
Current research in iris recognition is moving towards enabling more relaxed acquisition conditions. This has effects on the quality of acquired images, with low resolution being a predominant issue. Here, we evaluate a superresolution algorithm used to reconstruct iris images based on Eigen-transformation of local image patches. Each patch is reconstructed separately, allowing better quality of enhanced images by preserving local information. Contrast enhancement is used to improve the reconstruction quality, while matcher fusion has been adopted to improve iris recognition performance. We validate the system using a database of 1,872 near-infrared iris images. The presented approach is superior to bilinear or bicubic interpolation, especially at lower resolutions, and the fusion of the two systems pushes the EER to below 5% for down-sampling factors up to a image size of only 13×13.
Year
DOI
Venue
2016
10.1109/BTAS.2016.7791208
2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS)
Keywords
Field
DocType
very low-resolution iris recognition,eigen-patch super-resolution algorithm,matcher fusion,image quality,iris image reconstruction,local image patch eigen-transformation,local information preservation,contrast enhancement,reconstruction quality improvement,near-infrared iris image database,EER,down-sampling factor,image size
Iterative reconstruction,Computer vision,Signal processing,Iris recognition,Pattern recognition,Computer science,Bicubic interpolation,Fusion,Artificial intelligence,Superresolution,Image resolution,Bilinear interpolation
Conference
ISSN
ISBN
Citations 
2474-9680
978-1-4673-9734-6
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Fernando Alonso-Fernandez153137.65
Reuben A. Farrugia211118.26
Josef Bigun342641.34