Paper Info
Title
Gabor Filtering and Adaptive Optimization Neural Network for Iris Double Recognition.
Abstract
The iris image is greatly affected by the collection environment, so, the outputs of different iris categories in the distance recognition algorithm may similar. Neural network recognition algorithm can improve the results distinction, but the same neural network structure has a great difference in the recognition effect of different iris libraries. They all may reduce the accuracy of iris recognition. This paper proposes an iris double recognition algorithm based on Gabor filtering and adaptive optimization neural network. Gabor filtering is used to extract iris features. Hamming distance is used to eliminate most of different categories in the first recognition. The BP neural network that connection weights are optimized by immune particle swarm optimization algorithm is used for the second recognition. The results that the proposed algorithm compares with many algorithms in different iris libraries show that the proposed algorithm can effectively improve iris recognition accuracy.
Year
DOI
Venue
2018
10.1007/978-3-319-97909-0_47
BIOMETRIC RECOGNITION, CCBR 2018
Keywords
Field
DocType
Iris double recognition,Gabor filtering,Adaptive optimization neural network,Hamming distance,Immune particle swarm optimization
Particle swarm optimization,Iris recognition,Adaptive optimization,Pattern recognition,Computer science,Filter (signal processing),Hamming distance,Artificial intelligence,Recognition algorithm,Artificial neural network
Conference
Volume
ISSN
Citations 
10996
0302-9743
0
PageRank 
References 
Authors
0.34
8
7
Name
Order
Citations
PageRank
Shuai Liu110529.14
Yuan-Ning Liu216022.94
Xiaodong Zhu37310.24
Zhen Liu412216.50
Guang Huo5126.10
Tong Ding600.68
Kuo Zhang710.69