Iris secondary recognition based on decision particle swarm optimization and stable texture

被引：0
作者：
Liu Y.-N. [1 ,2 ]
Liu S. [1 ,3 ]
Zhu X.-D. [1 ,2 ]
Huo G. [4 ]
Ding T. [1 ,3 ]
Zhang K. [1 ,2 ]
Jiang X. [1 ,3 ]
Guo S.-J. [1 ,2 ]
Zhang Q.-X. [1 ,3 ]
机构：
[1] Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun
[2] College of Computer Science and Technology, Jilin University, Changchun
[3] College of Software, Jilin University, Changchun
[4] College of Computer Science, Northeast Electric Power University, Jilin
来源：
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2019年 / 49卷 / 04期
关键词：
Computer application; Decision particle swarm optimization; Iris secondary recognition; Markov decision process; Stable texture;
D O I：
10.13229/j.cnki.jdxbgxb20180487
中图分类号：
学科分类号：
摘要：
The collection statuses of the iris image are different at different times, so the accuracy of single recognition algorithm in the multi-category iris recognition may be poor.This paper proposes an iris secondary recognition algorithm based on decision particle swarm optimization and stable texture. Use six image processing algorithms to extract stable texture features.The Gabor filtering and Hamming distance constitute the first recognition, and the Haar wavelet and BP neural network constitute the second recognition, complete secondary recognition of multi-category irises by sequence structure.Gabor filtering and neural network are adaptively optimized according to the Markov decision process and different iris libraries.The results show that the proposed algorithm can effectively improve accuracy of iris recognition. © 2019, Jilin University Press. All right reserved.
引用
收藏
页码：1329 / 1338
页数：9
相关论文
共 34 条
[31]  
Li H.-L., Guo L.-H., Li X.-M., Et al., Iris recognition based on SCCS-LBP, Optics and Precision Engineering, 21, 8, pp. 2129-2136, (2013)
[32]  
Tan C.W., Kumar A., Accurate iris recognition at a distance using stabilized iris encoding and zernike moments phase features, IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 23, 9, pp. 3962-3974, (2014)
[33]  
He F., Han Y., Wang H., Et al., Deep learning architecture for iris recognition based on optimal Gabor filters and deep belief network, Journal of Electronic Imaging, 26, 2, (2017)
[34]  
Nalla P.R., Kumar A., Toward more accurate iris recognition using cross-spectral matching, IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 26, 1, (2016)
1234