Name
Abstract | ||
|---|---|---|
Personal identification using iris images has invited lots of attention in the literature and offered higher accuracy. However, the computational complexity in the feature extraction from the normalized iris images is still of key concern and further efforts are required to develop efficient feature extraction approaches. In this paper, we investigate a new approach for the efficient and effective extraction of iris features using localized Radon transforms. The feature extraction process exploits the orientation information from the local iris texture features using finite Radon transform. The dominant orientation from these Radon transform features is used to generate a binarized/compact feature representation. The similarity between two feature vectors is computed from the minimum matching distance that can account for the variations resulting from translation and rotation of the images. The feasibility of this approach is rigorously evaluated on two publically available iris image databases, i.e. IITD iris image database v1 and CASIA v3 iris image database. We also investigate the multi-scale analysis of iris images to enhance the performance. The experimental results presented in this paper are highly promising and suggest the computationally attractive alternative for the online iris identification. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICPR.2010.696 | ICPR |
Keywords | Field | DocType |
iris recognition,localized radon transform,feature vector,biometrics,personal identification,online iris identification,iris images,iris texture feature,feature extraction,iris image,nonlinear fusion,minimum matching distance,compact feature representation,radon transforms,iris feature,normalized iris image,multi-scale iris authentication,finite radon transform,efficient feature extraction approach,publically available iris image,multiscale analysis,local iris texture,iris image database,iris,computational complexity,radon transform,pixel | Computer vision,Iris recognition,Feature vector,Normalization (statistics),Pattern recognition,Computer science,Feature extraction,Pixel,Artificial intelligence,Biometrics,Radon transform,Computational complexity theory | Conference |
ISSN | ISBN | Citations |
1051-4651 | 978-1-4244-7542-1 | 14 |
PageRank | References | Authors |
0.64 | 10 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yingbo Zhou | 1 | 263 | 19.43 |
| Ajay Kumar | 2 | 14 | 0.64 |