基于多方向局部二值模式与稳定特征的虹膜识别

doi:10.13229/j.cnki.jdxbgxb20191176

Abstract

Abstract:

To improve the iris recognition rate by suppressing the noise and extracting the feature of local texture effectively in iris image， an iris recognition algorithm based on multi-direction local binary mode and stable feature is proposed. First， applying a variety of filtering processing algorithms， sufficient iris internal features are extracted from the iris image in unstable state with illumination and noise interference， which are as effective iris information to form a stable iris feature image. Then， based on the local binary pattern， a multi-directional local binary pattern is proposed to extract the feature vectors. This method not only can describe the overall spatial information of the iris image， but also reduce the dimension of the feature image. Finally， the Hamming distance is used for iris recognition. This method is used to identify CASIA， JLU-6.0 database， and the results show that this method not only can guarantee the effectiveness of the stable feature， but also extract the local texture feature effectively with higher recognition rate and robustness.

Key words: local texture, iris recognition, local binary pattern, robustness

CLC Number:

TP391.41

Xiao-dong ZHU,Qi-xian ZHANG,Yuan-ning LIU, WU-di,Zu-kang WU,Chao-qun WANG,Xin-long LI. Iris recognition based on multi⁃direction local binary pattern and stable feature[J].Journal of Jilin University(Engineering and Technology Edition), 2021, 51(2): 650-658.

Figures/Tables 17

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Table 1

Table 2

Fig.9

Table 3

Experimental results of JLU-6.0"

虹膜库	项目	4 $×$ 8	16 $×$ 8	32 $×$ 8	32 $×$ 16	64 $×$ 16
JLU-6.0 (T4)	识别率/%	93.4	93.2	89.7	84.5	80.1
JLU-6.0 (T4)	时间/s	9.5	5.2	4.4	3.2	2.6
JLU-6.0 (T6)	识别率/%	95.1	94.9	90.7	86.4	83.6
JLU-6.0 (T6)	时间/s	11.6	7.3	5.4	4.8	3.3
JLU-6.0 (T8)	识别率/%	96.8	96.2	92.4	87.7	85.3
JLU-6.0 (T8)	时间/s	12.7	8.2	7.5	6.2	4.9
JLU-6.0 (T10)	识别率/%	98.2	97.6	95.3	91.6	87.3
JLU-6.0 (T10)	时间/s	15.5	10.8	9.2	8.4	6.9

Table 3

Table 4

Experimental results of CASIA-Interval"

虹膜库	项目	4 $×$ 8	16 $×$ 8	32 $×$ 8	32 $×$ 16	64 $×$ 16
CASIA- Interval(T4)	识别率/%	87.8	85.7	84.6	82.3	80.4
CASIA- Interval(T4)	时间/s	6.6	4.2	3.5	2.7	1.4
CASIA- Interval(T6)	识别率/%	89.8	86.4	85.6	82.9	81.1
CASIA- Interval(T6)	时间/s	7.5	4.9	4.6	3.8	2.1
CASIA- Interval(T8)	识别率/%	90.6	87.2	85.9	83.7	82.5
CASIA- Interval(T8)	时间/s	7.9	5.3	5.0	4.2	3.4
CASIA- Interval(T10)	识别率/%	92.2	90.3	87.8	85.4	83.6
CASIA- Interval(T10)	时间/s	9.2	6.6	5.7	5.0	4.1

Table 4

Fig.10

Table 5

Table 6

Fig.11

References 23

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稳定特征处理算法	CRR/%	EER/%
不做处理	95.21	3.23
高斯滤波	97.72	1.74
高斯拉普拉斯滤波和中值滤波	99.04	0.85
本文算法	99.42	0.53

识别算法	JLU-6.0		CASIA-Iris- Interval		CASIA-Iris-Lamp
识别算法	CRR	EER	CRR	EER	CRR	EER
LBP	94.52	2.32	96.12	2.17	91.34	3.94
MB-LBP	94.27	2.59	97.98	2.14	94.93	2.25
CS-LBP	96.67	1.66	98.17	1.53	95.25	2.45
SCCS-LBP	97.53	0.96	98.95	1.16	96.43	0.98
MD-LBP	99.28	0.43	90.42	2.48	98.26	0.67

Iris recognition based on multi⁃direction local binary pattern and stable feature

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