Tools
Tools
Kolivand, H, Akintoye, KA, Asadianfam, S and Rahim, MS (2023) Improved methods for finger vein identification using composite Median-Wiener filter and hierarchical centroid features extraction. Multimedia Tools and Applications. ISSN 1380-7501
|
Text
s11042-023-14469-z (1).pdf - Published Version Available under License Creative Commons Attribution. Download (3MB) | Preview |
Abstract
Finger vein patterns contain highly discriminative characteristics, which are difficult to be forged due to residing underneath the skin. Several pieces of research have been carried out in this field but there is still an unresolved issue when data capturing and processing is of low quality. Low-quality data have caused errors in the feature extraction process and reduced identification performance rate in finger vein identification. The objective of this paper is to address this issue by presenting two methods, a new image enhancement, and a feature extraction method. The image enhancement, Composite Median-Wiener (CMW) filter, improves image quality and preserves the edges. Moreover, the feature extraction method, Hierarchical Centroid Feature Method (HCM), is fused with the statistical pixel-based distribution feature method at the feature-level fusion to improve the performance of finger vein identification. These methods were evaluated on public SDUMLA-HMT and FV-USM finger vein databases. Each database was divided into training and testing sets. The average result of the experiments conducted was taken to ensure the accuracy of the measurements. The k-Nearest Neighbor classifier with city block distance to match the features was implemented. Both these methods produced accuracy as high as 97.64% for identification rate and 1.11% of equal error rate (EER) for measures verification rate. These showed that the accuracy of the proposed finger vein identification method is higher than the existing methods. The results have proven that the CMW filter and HCM have significantly improved the accuracy of finger vein identification.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | 0801 Artificial Intelligence and Image Processing; 0803 Computer Software; 0805 Distributed Computing; 0806 Information Systems; Artificial Intelligence & Image Processing; Software Engineering |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Computer Science & Mathematics |
| Publisher: | Springer |
| Related URLs: | |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 21 Feb 2023 12:02 |
| Last Modified: | 18 May 2023 11:10 |
| DOI or ID number: | 10.1007/s11042-023-14469-z |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/18948 |
 |
View Item |