Paper Info
Title
Iris double recognition based on modified evolutionary neural network
Abstract
Aiming at multicategory iris recognition under illumination and noise interference, this paper proposes a method of iris double recognition based on a modified evolutionary neural network. An equalization histogram and Laplace of Gaussian operator are used to process the iris to suppress illumination and noise interference and Haar wavelet to convert the iris feature to binary feature encoding. Calculate the Hamming distance for the test iris and template iris, and compare with classification threshold, determine the type of iris. If the iris cannot be identified as a different type, there needs to be a secondary recognition. The connection weights in back-propagation (BP) neural network use modified evolutionary neural network to adaptively train. The modified neural network is composed of particle swarm optimization with mutation operator and BP neural network. According to different iris libraries in different circumstances of experimental results, under illumination and noise interference, the correct recognition rate of this algorithm is higher, the ROC curve is closer to the coordinate axis, the training and recognition time is shorter, and the stability and the robustness are better. (C) 2017 SPIE and IS&T
Year
DOI
Venue
2017
10.1117/1.JEI.26.6.063023
JOURNAL OF ELECTRONIC IMAGING
Keywords
Field
DocType
iris double recognition,evolutionary neural network,equalization histogram,Laplace of Gaussian operator,Haar wavelet,mutation operator
Particle swarm optimization,Computer vision,Histogram,Iris recognition,Pattern recognition,Computer science,Robustness (computer science),Hamming distance,Artificial intelligence,Haar wavelet,Artificial neural network,Encoding (memory)
Journal
Volume
Issue
ISSN
26
6
1017-9909
Citations 
PageRank 
References 
1
0.37
8
Authors
6
Name
Order
Citations
PageRank
Shuai Liu110529.14
Yuanning Liu210.71
Xiaodong Zhu37310.24
Guang Huo4126.10
Wen-Tao Liu511.05
Jia-Kai Feng610.37