Feature Fusion Using GSA for Multi- Instance Authentication System
| dc.contributor.author | Janet O. Jooda | |
| dc.date.accessioned | 2025-05-28T15:42:16Z | |
| dc.date.available | 2025-05-28T15:42:16Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | Multi-instance fusion of fingerprint authentication system at score level overcomes a few of the shortcomings of a Unimodal Biometric System (UBS) and enhanced the efficiency of the system. However, due to loss of information at higher levels, the features fused at the score level are confined in comparison to feature level fusion and could lead to poor performance. In this study, multi-instance fusion of fingerprints was done at feature level using Gravitational Search Algorithm (GSA) to select and combine minimal relevant informative texture features subsets from multi- instances of fingerprint and considerably improves the performance of the system. The approach was validated by creation of multi-instances of fingerprint database acquired locally from 150 subjects in an uncontrolled environment and texture based feature extraction was considered and classification of fused texture feature was done using back propagation neural network. The results show that the presented technique was effective in subject authentication with accuracy of 97.09%, indicating that it can successfully secure fingerprint authentication systems from unauthorized attacks. | |
| dc.identifier.citation | Jooda et al.; Asian Res. J. Curr. Sci., vol. 5, no. 1, pp. 259-268, 2023; | |
| dc.identifier.uri | https://repository.run.edu.ng/handle/123456789/4945 | |
| dc.language.iso | en | |
| dc.publisher | Asian Research Journal of Current Science | |
| dc.relation.ispartofseries | Volume 5, ; Issue 1 | |
| dc.title | Feature Fusion Using GSA for Multi- Instance Authentication System | |
| dc.type | Article |