Computer Science ›› 2021, Vol. 48 ›› Issue (6A): 57-62.doi: 10.11896/jsjkx.200900218

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Research on Iris Recognition Algorithm Based on Wavelet Packet Decomposition

ZHOU Jun1,2, WANG Shuai3, LIU Fan-yi4   

  1. 1 Department of Information Engineering,Army Service College,Chongqing 401331,China
    2 Chongqing Modern Service Industry Research Center,Chongqing 401331,China
    3 Basic Laboratory Center,Army Service College,Chongqing 401331,China
    4 Chongqing Jiaotong University,Chongqing 400074,China
  • Online:2021-06-10 Published:2021-06-17
  • About author:ZHOU Jun,born in 1981,doctoral candidate,senior engineer,is a member of China Computer Federation.His main research interests include signal processing and artificial intelligence.
  • Supported by:
    Science and Technology Research Program of Chongqing Municipal Education Commission(KJZD-K201904401,KJZD-K202004401) and Artificial Intelligence Application Collaborative Innovation Center of Chongqing Business Vocational College.

Abstract: Iris feature extraction is the key step in iris recognition.The wavelet method does not further decompose the high-frequency space when decomposing the iris image,but the iris features are more contained in the high-frequency space,and the extracted iris features are insufficient in the feature expression capabilities.Aiming at the above problems,an iris feature recognition method based on wavelet packet multi-scale decompositionis proposed in this paper,diagonal high-frequency subband map from the second layer based on wavelet packet de-composition is modulated into iris feature code,and the feature is recognized through hamming distance.In the experiment,sym2 wavelet is used as decomposition wavelet function,which carries out 5350 times of feature matching.The results show that the correct recognition rate is 98.5%,which is superior to the wavelet zero crossing method of boles and the two-dimensional Haar wavelet transform method of Lim,is second only to the two-dimensional Gabor method of Daugman.

Key words: Decomposition, Diagonal high-frequency, Iris features, Threshold, Wavelet packet

CLC Number: 

  • TP391.5
[1] TIAN Q C,ZHANG R S.Overview of biometric recog-nition technology [J].Computer Application Research,2010,26(12):4401-4410.
[2] DAUGMAN J.High confidence visual recognition of persons by a test of statistical independence[J],IEEE Transactions on Pattern Analysis Machine Intelligence,1993,15(11):1148-1161.
[3] WILDES R,ASMUTH J C.A system for automated iris recognition[C]//Proceedings of the 2nd IEEE Workshop on Applicant Computer Vision.Sarasota,FL USA,1994:121-128.
[4] HE F,HAN Y,WANG H,et al.Deep learning architecture for iris recognition based on optimal Gabor filters and belief network[J].Journal of Electronic Imaging,2017,26(2):023005.
[5] YANG X,ZHU X D,LIU Y N,et al.Iris recognition methodbased on block wavelet feature combined with BP neural network [J].Computer Engineering and Applications,2019,55(18):132-139.
[6] ZHENG Y P,LI G Y,LI Y.A review of research on the application of deep learning in image recognition[J].Computer Engineering and Applications,2019,55(12):20-36.
[7] LIMS S,LEE K,BYEON O,et al.Efficient iris recognitionthrough improvement of feature vector and classifier[J].ETRI Journal,2001,23(2):61-70.
[8] BOLES W W,BOASHASH B.A human identification techni-que using images of the iris and wavelet transform[J].IEEE Transactions on Signal Processing,1998,46(4):1185-1188.
[9] BAIY C.Research on iris recognition method of visible lightmobile terminal [D].Shenyang:Shenyang University of Technology,2019.
[10] KANG B J,PARK K R.A robust eyelash detection based on iris focus assessment [J].Pattern Recognition,2007,28(13):1630-1639.
[11] DAUGMAN J.Statistical richness of visual phase information:Update on recognizing persons by iris patterns[J].International Journal of Computer Vision,2001,45(1):25-38.
[12] MA L,TAN T,WANG Y,et al.Personal Recognition Based on Iris Texture Analysis[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(12):1519-1533.
[13] MASEK L.Recognition of human iris patterns for biometricidentification[R].Technical Report,School of Computer Science and Software Engineering,University of Western Australia,2003.
[14] YANG J G.Wavelet analysis and its engineering application[M].Beijing:Mechanical Industry Press,2009.
[15] SUNG H,LIM J,PARK J.Iris Recognition Using CollaretteBoundary Localization[C]//Proceedings of the 17th International Conference on Pattern Recognition.Washington,DC,USA 2004:857-860.
[16] FU H Z,XU Y W,LIN S,et al.Angle-Closure Detection in Anterior Segment OCT Based on Multilevel DeepNetwork[J].IEEE Transactions on Cybernetics,2020,50(7).
[17] LIU S,LIU Y N,ZHU X D,et al.Iris double recognition based on modified evolutionary neural network[J].Journal of Electronic Imaging,2017,26(6):063023.
[18] TIAN Q C.Principle and Algorithm of Iris Recognition [M].Beijing:National Defense Industry Press,2010.
[19] JIANG H,MENG D T.Iris recognition based on CS-LBPand adaptive neural network[J].Journal of Northeast Normal University (Natural Science Edition),2018,50(3):58-64.
[20] Institute of Automation,Chinese Academy of Sciences.CASIA iris image database [EB/OL].(2007-12-23).http://www.cbsr.ia.ac.cn/English/Irisdatabase.asp.
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