Journal of Jilin University(Engineering and Technology Edition) ›› 2019, Vol. 49 ›› Issue (4): 1329-1338.doi: 10.13229/j.cnki.jdxbgxb20180487

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

Yuan-ning LIU1,2(),Shuai LIU1,3,Xiao-dong ZHU1,2(),Guang HUO4,Tong DING1,3,Kuo ZHANG1,2,Xue JIANG1,3,Shu-jun GUO1,2,Qi-xian ZHANG1,3   

  1. 1. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
    2. College of Computer Science and Technology, Jilin University, Changchun 130012, China
    3. College of Software, Jilin University, Changchun 130012, China
    4. College of Computer Science, Northeast Electric Power University, Jilin 132012, China
  • Received:2018-05-22 Online:2019-07-01 Published:2019-07-16
  • Contact: Xiao-dong ZHU E-mail:lyn@jlu.edu.cn;zhuxd@jlu.edu.cn

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Abstract:

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.

Key words: computer application, iris secondary recognition, stable texture, Markov decision process, decision particle swarm optimization

CLC Number: 

  • TP391.41

Fig.1

Iris recognition process"

Fig.2

Process of iris image processing"

Fig.3

Difference images"

Fig.4

The stable texture feature image"

Fig. 5

The stable texture feature image"

Fig. 6

Process of Gabor filtering optimization"

Fig.7

Process of neural network optimization"

Table 1

Changing situation of threshold"

序号D1D2MF
1增加增加增加增加
2增加增加增加减小
3增加增加减小增加
4增加减小增加增加
5减小增加增加增加
6减小减小增加增加
7减小增加减小增加
8减小增加增加减小
9增加减小减小增加
10增加减小增加减小
11增加增加减小减小
12增加减小减小减小
13减小增加减小减小
14减小减小增加减小
15减小减小减小增加
16减小减小减小减小

Table 2

The match number of time experiment"

虹膜库类别数单类别图像数图像总数匹配数
类间类外总数
JLU?4.015020030 00060 000150 000210 000
Iris?Lamp4114016 44030 000100 000130 000

Table 3

The results of time experiment"

算法JLU-4.0CASIA-Iris-Lamp
CRR/%EER/%CRR/%EER/%
Daugman93.483.8494.263.27
Lim94.593.4894.133.16
Yao98.751.2498.231.79
Donald98.131.9298.531.51
Li98.651.3698.421.44
二次识别99.470.7399.160.94

Table 4

The match number of performance experiment"

虹膜库类别数单类别图像数图像总数匹配数
类间类外总数
JLU?5.010010010 00016 00051 30067 300
Iris?Interval200153 0008 56525 46234 027

Fig. 8

The ROC curve of JLU?5.0"

Table 5

The results of performance experiment"

算法JLU?5.0Iris?Interval
CRR/%EER/%CRR/%EER/%
决策优化+二次识别97.132.9598.621.47
稳定特征+二次识别98.421.6598.961.05
Zernike矩相位特征98.781.3697.832.63
深度学习架构96.453.6497.033.19
交叉光谱匹配97.842.6198.271.75
稳定特征+决策优化+二次识别99.210.8999.430.78

Fig. 9

The ROC curves of CASIA?Iris?Interval"

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