Name
Abstract | ||
|---|---|---|
Biometric systems are widely used in applications such as forensics and military. Biometric authentication is a challenging and complex task. These biometric systems must be accurate for practical applications. In this era of artificial intelligence, artificial neural network-based classifiers are widely used in biometric-based systems. However, most of the artificial neural network-based classifiers are less accurate and computationally complex. In this work, two modified self-organising map (SOM) networks are proposed for iris image classification to improve the performance measures. Particle swarm optimization technique is used in the training process of conventional SOM. The experiments are carried out with conventional and modified classifiers. The proposed modified classifiers provide better performance than the conventional SOM classifier. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1111/exsy.12467 | EXPERT SYSTEMS |
Keywords | DocType | Volume |
biometric system, classifier, iris image, machine learning, optimization, self-organising map | Journal | 38 |
Issue | ISSN | Citations |
1 | 0266-4720 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. Jenkin Winston | 1 | 4 | 1.11 |
| D. Jude Hemanth | 2 | 119 | 22.74 |