应用统计距离实现虹膜定位

陈传虎; 邹德旋; 刘海宽

doi:10.3788/OPE.20122011.2516

您当前的位置：

首页 >

文章列表页 >

应用统计距离实现虹膜定位

信息科学 | 更新时间：2020-08-12

- 应用统计距离实现虹膜定位
- Iris location algorithm by counting distances
- 光学精密工程 2012年20卷第11期页码：2516-2522
- 作者机构：
  
  江苏师范大学电气工程及自动化学院,江苏徐州,221116
- 作者简介：
- 基金信息：
  
  江苏省省高校自然科学研究项目(No.11KJB510026)();江苏省自然科学基金资助项目(No. BK2011205)()
- DOI：10.3788/OPE.20122011.2516
  中图分类号： TP391.41
- 收稿日期：2012-06-20，
  
  修回日期：2012-08-30，
  
  纸质出版日期：2012-11-10
- 稿件说明：
移动端阅览
陈传虎, 邹德旋, 刘海宽. 应用统计距离实现虹膜定位[J]. 光学精密工程, 2012,20(11): 2516-2522

CHEN Chuan-hu, ZOU De-xuan, LIU Hai-kuan. Iris location algorithm by counting distances[J]. Editorial Office of Optics and Precision Engineering, 2012,20(11): 2516-2522
陈传虎, 邹德旋, 刘海宽. 应用统计距离实现虹膜定位[J]. 光学精密工程, 2012,20(11): 2516-2522 DOI： 10.3788/OPE.20122011.2516.

CHEN Chuan-hu, ZOU De-xuan, LIU Hai-kuan. Iris location algorithm by counting distances[J]. Editorial Office of Optics and Precision Engineering, 2012,20(11): 2516-2522 DOI： 10.3788/OPE.20122011.2516.

摘要

提出了一种统计距离法(MCD)来提高虹膜识别效率。用MCD对虹膜边缘图像去噪

并建立了有效的虹膜内外圆定位方法。首先

用灰度求和找到瞳孔内一点

同时

使用Canny算子获得虹膜边缘图像

并根据所确定的瞳孔内的点来寻找虹膜内边缘上的3个边缘点。将这3个边缘点代入圆的方程即可找到虹膜内边缘。然后

根据虹膜内定位数据来消除虹膜外边缘图像中的噪声

再在去噪后的边缘图像中排除20%个与虹膜内圆圆心距离最远的边缘点。最后

使用一种改进的Hough变换来获得虹膜外边缘数据。实验结果表明:MCD的内定位时间和外定位时间分别是0.122 2 s和0.242 4 s

均比快速精确虹膜定位法(FAILA)低

且成功率为96.67%

高于FAILA。因此

MCD具有较高的精度和较快的速度

可提高虹膜识别的效率。

Abstract

A Method of Counting Distances (MCD) was proposed to improve the efficiency of iris recognition in this paper. The MCD was used in a denoising for iris edge image

and efficient location methods for both inner and outer circles of the iris were established. First

a method of grey level summation was utilized to find a pixel in the pupil

meanwhile

the iris edge image could be obtained by using the Canny operator

and three edge points of iris inner edge could be found according to the pixel in the pupil. Then

three edge points were derived to the equation of a circle to find the iris inner edge by substituting. Furthermore

the noises in iris outer edge image were eliminated in terms of the inner location data

and 20 percent of the edge points far from the center of inner circle of the iris were excluded from the denoised edge image. Finally

an improved Hough transformation was used to obtain the data of iris outer edge. Experimental results show that both inner and outer location time obtained using MCD is lower than those obtained by the Fast and Accurate Iris Location Algorithm (FAILA)

and the success rate of MCD is 96.67%

which is higher than that of FAILA. Thus

the MCD has high precision and fast speed

and can lay the foundation for improving the efficiency of iris recognition.

关键词

Keywords

references

JIANG X B, YOU X G, YUAN Y, et al.. A me thod using long digital straight segments for fingerprint recognition [J]. Neurocomputing, 2012, 77(1): 28-35.[2] ANDICS A, MCQUEEN J M, PETERSSON K M, et al.. Neural mechanisms for voice recognition [J]. Neuro Image, 2010, 52(4): 1528-1540.[3] PAN X, RUAN Q Q. Palmprint recognition using Gabor-based local invariant features [J]. Neurocomputing, 2009, 72(7-9): 2040-2045.[4] 杨利平,龚卫国,李伟红,等. 随机采样子空间保局投影人脸识别算法[J]. 光学精密工程, 2008, 16(8): 1465-1470. (in Chinese) YANG L P, GONG W G, LI W H, et al.. Random sampling subspace locality preserving projection for face recognition [J]. Opt. Precision Eng., 2008, 16(8): 1465-1470.[5] 苑玮琦,徐露,林忠华. 基于相位一致性最大响应方向的虹膜识别方法[J]. 光学精密工程, 2009, 17(1): 179-184. (in Chinese) YUAN W Q, XU L, LIN ZH H. Iris recognition based on maximal responding orientation of phase congruency [J]. Opt. Precision Eng., 2009, 17(1): 179-184.[6] ABHYANKAR A, SCHUCKERS S. A novel bior- thogonal wavelet network system for off-angle iris recognition [J]. Pattern Recognition, 2010, 43(3): 987-1007.[7] DAUGMAN J G. Recognizing persons by their iris. Information Security Technical Report, 1998,3(1), 33-39.[8] WILDES R P. Iris recognition: an emerging biometric technology. Proceeding of the IEEE, 1997, 85(9): 1348-1363.[9] 苑玮琦,徐露,林忠华. 一种新的虹膜图像预处理方法[J]. 光电子激光, 2009, 20(2): 234-239. (in Chinese) YUAN W Q, XU L, LIN ZH H. A novel method of iris image preprocessing [J]. Journal of Optoelectronics.Laser, 2009, 20(2): 234-239. (in Chinese)[10] LU CH H, LU ZH Y. Local feature extraction for iris recognition with automatic scale selection [J]. Image and Vision Computing, 2008, 26(7): 935-940.[11] TAN T N, ZHANG X B, SUN ZH N, et al.. Noisy iris image matching by using multiple cues [J]. Pattern Recognition Letters, 2012, 33(8): 970-977.[12] DAUGMAN J G. High confidence visual recognition of persons by a test of statistical independence [J]. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1993, 15(11): 1148-1161.[13] DAUGMAN J. How iris recognition works [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2004, 14(1): 21-30.[14] WILDES R. A system for automated iris recognition. Proceedings of the Second IEEE Workshop on applications of Computer Vision, 1994: 121-128.[15] WILDES R, ASMUTH J, GREEN G, et al.. A machine-vision system for iris recognition [J]. Machine Vision and Applications, 1996, 9: 1-8.[16] TAN T N, HE ZH F, SUN ZH N. Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition [J]. Image and Vision Computing, 2010, 28(2): 223-230.[17] 吴建华,邹德旋,李静辉. 一种快速精确的虹膜定位方法 [J]. 仪器仪表学报, 2007,28 (8): 1469-1473. WU J H, ZOU D X, LI J H. Fast and accurate iris location algorithm [J]. Chinese Journal of Scientific Instrument, 2007, 28(8): 1469-1473. (in Chinese)[18] 中国科学院自动化研究所. CASIA虹膜图像数据库. Institute of Automation Chinese Academy of Sciences. database of CASIA iris image. (in Chinese)[19] WILCOXON F. Individual comparisons by ranking methods [J]. Biometrics, 1945, 1: 80-83.

浏览量

388

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

三次透视投影变换图像拼接

基于统计特征中心对称局部二值模式的虹膜识别

基于PCHIP-LMD的虹膜识别方法

应用迭代圆环像素率法实现快速虹膜定位

基于小波过零点位置不等长编码匹配的虹膜识别