Name
Title | ||
|---|---|---|
Iris Recognition Based on Adaptive Optimization Log-Gabor Filter and RBF Neural Network. |
Abstract | ||
|---|---|---|
In order to improve the universality and accuracy of one-to-one iris recognition algorithm, there proposes an iris recognition algorithm based on adaptive optimization Log-Gabor filter and RBF neural network in this paper. Iris amplitude features are extracted with Log-Gabor filter. The selection mutation operator and particle swarm optimization algorithm are used to optimize the filter parameters. Then principal component analysis (PCA) are used to reduce dimensions, thereby reducing the noise and redundancy. Then the Euclidean distance between iris amplitude features are calculated, and the RBF neural network is built for iris recognition. Compared with other iris recognition algorithms on JLU-6.0 iris library and CASIA-Iris-Interval iris library, the recognition rate of this algorithm is higher, and the ROC curve is closer to the coordinate axis, so it has good stability and robustness. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-31456-9_35 | BIOMETRIC RECOGNITION (CCBR 2019) |
Keywords | Field | DocType |
Log-Gabor filter,Selection mutation operator,RBF neural network,Euclidean distance,Iris recognition | Particle swarm optimization,Iris recognition,Adaptive optimization,Pattern recognition,Computer science,Euclidean distance,Robustness (computer science),Redundancy (engineering),Artificial intelligence,Log Gabor filter,Artificial neural network | Conference |
Volume | ISSN | Citations |
11818 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 0 | 10 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qixian Zhang | 1 | 0 | 0.68 |
| Xiaodong Zhu | 2 | 73 | 10.24 |
| Yuan-Ning Liu | 3 | 160 | 22.94 |
| Guang Huo | 4 | 12 | 6.10 |
| Guangyu Wang | 5 | 0 | 0.68 |
| Shuai Liu | 6 | 105 | 29.14 |
| Tong Ding | 7 | 0 | 0.34 |
| Kuo Zhang | 8 | 0 | 0.34 |
| Kiese Diangebeni Reagan | 9 | 0 | 0.34 |
| Chaoqun Wang | 10 | 8 | 4.84 |