Home| Contact Us| New Journals| Browse Journals| Journal Prices| For Authors|

Print ISSN: 0976-4127
Online ISSN:
0976-4135


  About JMPT
  DLINE Portal Home
Home
Aims & Scope
Editorial Board
Current Issue
Next Issue
Previous Issue
Sample Issue
Upcoming Conferences
Self-archiving policy
Alert Services
Be a Reviewer
Publisher
Paper Submission
Subscription
Contact us
 
  How To Order
  Order Online
Price Information
Request for Complimentary
Print Copy
 
  For Authors
  Guidelines for Contributors
Online Submission
Call for Papers
Author Rights
 
 
RELATED JOURNALS
Journal of Digital Information Management (JDIM)
International Journal of Computational Linguistics Research (IJCL)
International Journal of Web Application (IJWA)

 

 
Journal of Multimedia Processing and Technologies
 

 

Feature Matching in Iris Recognition System using MATLAB
Imran N, Narendra Kumar Rao B
Sree Vidyanikethan Engineering College (Autonomous) Tirupati, Chittor District Andhra pradesh, India
Abstract: Iris recognition system is a secure human authentication in biometric technology. Iris recognition system consists of five stages. They are Feature matching, Feature encoding, Iris Normalization, Iris Segmentation and Image acquisition. In Image acquisition, the eye Image is captured from the CASIA database, the Image must have good quality with high resolution to process next steps. In Iris Segmentation, the Iris part is detected by using Hough transform technique and Canny Edge detection technique. Iris from an eye Image segmented. In normalization, the Iris region is converted from the circular region into a rectangular region by using polar transform technique. In feature encoding, the normalized Iris can be encoded in the form of binary bit format by using Gabor filter techniques. In feature matching, the encoded Iris template is compared with database eye Image of Iris template and generated the matching score by using Hamming distance technique. Based on the matching score, we get the result. This project is developed using Image processing toolbox of Matlab software.
Keywords: Iris Recognition, Biometric Identification, Feature matching, Iris Normalization, Image Acquisition Feature Encoding, Iris Segmentation Feature Matching in Iris Recognition System using MATLAB
DOI:https://doi.org/10.6025/jmpt/2020/11/2/43-59
Full_Text   PDF 1.18 MB   Download:   345  times
References:

[1] Caroline, Houston. (2010). Iris Segmentation and Recognition Using Circular Hough Transform and Wavelet Features, p 1-4, 2010.
[2] Matsoso, Samuel Monaheng., Padmaja, Kuruba., Iris Recognition using Circular Hough Transform, International Journal of Innovative Research in Science, Engineering and Technology, ISSN: 2319-8753, 2 (8) p 3546-3553, August 2013.
[3] Dubey, R. B., Abhimanyu Madan. (2014). “Iris Localization using Daugman’s Intero-Differential Operator, International Journal of Computer Applications, ISSN: 0975 – 8887, 93 (3) 6-12, May 2014.
[4] Djoumessi, Maeva. (2010). Iris Segmentation using Daugman’s Integro-Differential Operator, 2010.
[5] Yuan, Weiqi., He, Wei (2005). A Novel Eyelash Detection Method for Iris Recognition, IEEE Engineering in Medicine and Biology 27th Annual Conference Shanghai, China, p 6536- 6539, September 1-4, 2005.
[6] Zhu, Wenyao., Zhao, Zemao., Wu, Yufeng (2016). An Algorithm of Eyelashes Detection for Iris Recognition, International Journal of Security and Its Applications, 10 (7) p 195-202, 2016.
[7] Adam, M., Rossant, F., Amiel, F., Mikovikova, B., Ea, T. (2008). Eyelid Localization for Iris Recognition, Radio Engineering, 17 (4) 82-85, December 2008.
[8] Radman, Abduljalil., Zainal, Nasharuddin., Ismai, Mahamod. (2013). Efficient Iris Segmentation Based on Eyelid Detection, Journal of Engineering Science and Technology, 8 (4) 399 – 405, 2013.
[9] Satish, Rapaka., Rajesh Kumar, P., Inteti, Praneeth. (2015). IRIS Recognition System Using Geodesic Active Contours for Non-Ideal IRIS Images, International Journal of Engineering Research in Electronics and Communication Engineering, 2 (6) 81-87, June 2015.
[10] Mohammed, A. M., Abdullah, S. S., Dlay, Woo, W. L. (2014). Fast and Accurate Pupil Isolation Based on Morphology and Active Contour, International Journal of Information and Electronics Engineering, Vol. 4, No. 6, p 418-422, November 2014.
[11] Jeong, Dae Sik., Cho, Dalho., Jo, Jihye., Bae, Min-Kyeong., Park, Min Woo., Lee, Eui Chul (2015). Compensation for Non-linear Iris Pattern Deformation based on the Tensile Properties of Iris, Wseas Transactions on Information Science and Applications, 3402, Volume 12, 315-323,2015.
[12] Wei. Zhuoshi., Tan, Tieniu., Sun, Zhenan. (2007). Nonlinear Iris Deformation Correction Based on Gaussian Model, Lee, S.-W., Li, S.Z. (Eds.): ICB 2007, LNCS 4642, p 780–789, 2007.
[13] Anicham, S., Murukesh, C. (2014). An Efficient Iris Recognition System Using Contourlet Transform and Neural Networks, International Journal of Innovative Research in Science, Engineering and Technology, 3 (4) 11876-11881, April 2014.
[14] Vatsa, Mayank., Singh, Richa., Noore, Afzel. (2008). Improving Iris Recognition Performance Using Segmentation, Quality Enhancement, Match Score Fusion, and Indexing, IEEE Transactions on Systems, p 1-15, 2008.
[15] Pardhi, Shrishti., Qureshi, Shahana Gajala. (2-15). Designing and Implementation of Iris recognition System Using Morphological Bridged Canny Edge Detection and KNN Classifier, International Journal of Engineering and Computer Science, 4 (6) 2604-12609, June 2015.
[16] Chitte, P. P., Rana, J. G., Bhambare, R. R., More, V. A., Kadu, R.A., Bendre, M. R. (2010). IRIS Recognition System Using ICA, PCA, Daugman’s Rubber Sheet Model Together, International Journal of Computer Technology and Electronics Engineering, ISSN 2249-6343, 2 (1) 16-23, 2010.
[17] Sanchez-Avila, C., Sanchez-Reillo, R. (2005). Two different approaches for Iris recognition using Gabor filters and multiscale zero-crossing representation, Pattern Recognition, Vol. 38, 231 – 240.
[18] Sathiyaraja, K., Dhineshkumar, M., Thiyagarajan, N. (2013). Iris Segmentation and Recognization Using Log Gabor Filter and Curvelet Transform, International Journal Of Engineering And Computer Science, ISSN:2319-7242, Volume 2, Issue 9, p 2709-2714, September 2013.
[19] Ei., Phyu Win., Nyein Aye. (2014). An Effective Iris Recognition System, International Conference on Advances in Engineering and Technology, p 29-30, March, 2014.
[20] Dalal, Sambita., Sahoo, Tapasmini. (2012). A Selective Feature Matching Approach for Iris Recognition, International Journal of Computer Applications, 41 (20) 34-39, March 2012.
[21] Aly, I., Desoky, Hesham A. Ali., Nahla B. Abdel-Hamid. (2012). Enhancing Iris recognition system performance Using templates fusion, Ain Shams Engineering Journal, 3, p. 133–140, 2012.
[22] Sharma, Kriti., Monga, Himanshu. (2014). Efficient Biometric Iris Recognition Using Hough Transform With Secret Key, International Journal of Advanced Research in Computer Science and Software Engineering, 4 (7) July 2014, p 632- 640.
[23] Wagh, Amit Madhukar., Todmal, Satish R. (2015). Eyelids, Eyelashes Detection Algorithm and Hough Transform Method for Noise Removal in Iris Recognition, International Journal of Computer Applications, Volume 112 – No. 3, February 2015.
[24] Verma, Prateek., Dubey, Maheedhar., Basu, Somak., Verma, Praveen. (2012). Hough Transform Method for Iris Recognition- A Biometric Approach, International Journal of Engineering and Innovative Technology (IJEIT), ISO 9001:2008 Certified, 1 (6) 43-48, June 2012.
[25] Singh, Naveen., Gandhi, Dilip., Singh, Krishna Pal. (2011). IRIS Recognition System Using A Canny Edge Detection And A Circular Hough Transform, International Journal of Advances in Engineering & Technology, 1 (2) 221-228, May 2011.
[26] Nithyanandam, S., Gayathri, K. S., Priyadarshini, P. L. K. (2011). A New IRIS Normalization Process for Recognition System With Cryptographic Techniques, IJCSI International Journal of Computer Science Issues, 8 (4)1, p 342-348, July 2011.
[27] Prashanth, C. R., Shashikumar, D. R., Raja, K. B., Venugopal, K. R., Patnaik, L. M. (2009). High Security Human Recognition System using Iris Images, International Journal of Recent Trends in Engineering, Vol. 1, No. 1, p 647-652, May 2009.
[28] Open source Libraries for eye images of CASIA database link: http://biometrics.idealtest.org/dbDetailForUser.do?id=4.
[29] Open source code Libraries for Iris Recognition System link1:http://www.peterkovesi.com/studentprojects/libor/ sourcecode.html. link2: http://www.advancedsourcecode.com/Iris.asp.


Home | Aim & Scope | Editorial Board | Author Guidelines | Publisher | Subscription | Previous Issue | Contact Us |Upcoming Conferences|Sample Issues|Library Recommendation Form|

 

Copyright © 2011 dline.info