BIOMETRIC PERSONAL IDENTIFICATION ON 2D WAVELET TRANSFORM AND CHI-SQUARED MODEL

Authors

  • Taiwo TundeAdeniyi Computer Science Department Federal University of Technology
  • Olatubosun Olabode Computer Science Department Federal University of Technology
  • Gabriel B. Iwasokun Computer Science Department Federal University of Technology
  • Samuel A. Oluwadare Computer Science Department Federal University of Technology
  • Raphael O. Akinyede Computer Science Department Federal University of Technology

DOI:

https://doi.org/10.24297/ijct.v14i9.3984

Keywords:

Daugman, Iris, 2DWave length, Chi-square, Identification

Abstract

Iris recognition system consists of image acquisition, iris preprocessing, iris segmentation and feature extraction with comparism (matching) stages. The biometric based personal identification using iris requires accurate iris segmentation for successful identification or recognition. Recently, several researchers have implemented various methods for segmentation of boundaries which will require a modification of some of the existing segmentation algorithms for their proper recognition. Therefore, this research presents a 2D Wavelet Transform and Chi-squared model for iris features extraction and recognition. Circular Hough Transform was used for the segmentation of the iris image. The system localizes the circular iris and pupil region and removes the occluding eyelids and eyelashes. The extracted iris region is normalized using Daugman’s rubber sheet model into a rectangular block with constant dimensions to account for imaging inconsistencies. Finally, the phase data (iris signature) from the 2D wavelet transform data is extracted, forming the biometric template. The chi-squared distance is employed for classification of iris templates and recognition. Implementing this model can enhance identification. Based on the designed system, an FAR (False Acceptances ratio) of 0.00 and an FRR (False Rejection Ratio) of 0.896 was achieved.

Downloads

Download data is not yet available.

References

1. Bakstein Eduard (2013). “Iris Recognition”, Retrieved from https://cw.fel.cvut.cz/wiki/media/ courses/a6m33bio/iris_1-4-2014.pdf. Accessed in August, 2014.
2. Boles W. and Boashash B. (1998).“A human identification technique using images of the iris and wavelet transform”, IEEE Trans. Signal Process. 46(4):1185-1188.
3. Canny John (1986). “A Computational Approach to Edge Detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence. 8(6).
4. Choi Seung-Seok, Yoon Sungsoo, Cha Sung-Hyuk and Tappert Charles C. (2010). “Use of histogram distances in Iris Authentication”, ICGST-GVIP Journal, 5(5).
5. Daugman J. (1993). "High confidence visual recognition of persons by a test of statistical independence", IEEE Trans. PAMI, 15: 1148-1161.
6. Daugman J. (2004), How Iris Recognition Works‖, IEEE Transactions on Circuits and Systems for Video Technology, 14(1): 21-30.John Daugman’s webpage, Cambridge University, Faculty of Computer Science & Technology, Cambridge. http://www.cl.cam.ac.uk/~jgd1000/. Accessed 12 May 2015
7. Daugman J. (2007). “New methods in iris recognition”, IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics. 37(5):1167–1175.
8. Poonam Dhankhar and Neha Sahu (2013). “A Review and Research of Edge Detection Techniques for Image Segmentation”, International Journal of Computer Science and Mobile Computing (IJCSMC).2(7);86 – 92.
9. Pierre Gravel, Gilles Beaudoin, and Jacques A. Guise. De (2004). “A Method for Modeling Noise in Medical Images”, IEEE Transactions On Medical Imaging, 23(10).
10. Gulmire Kshamaraj, Ganorkar Sanjay (2012). “Iris Recognition Using Independent Component Analysis”, International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com ISSN 2250-2459.2(7).
11. He Zhaofeng, Tan Tieniu, Sun Zhenan and Qiu Xianchao, (2008). “Towards Accurate and Fast Iris Segmentation for Iris Biometrics”, Pattern Analysis and Machine Intelligence (Transactions on IEEE), 31(9).
12. Ioannou Dimitrios, Huda Walter, Laine Andrew F. (1999). “Circle recognition through a 2D Hough Transform and radius histogramming”, Image and Vision Computing, 17: 15–26.
13. Jain A. K., Hong L., Pankanti S., and Bolle R. (1997). “An Identity-Authentication System Using Finger-prints”, In Proceeding of the IEEE. 85: 1365-1388. 14. James, Matthew (2005). “An Introduction to Edge Detection”. Retrieved from www.generation5.org/content /2002/im01.asp. Accessed on Aug 2014.
15. Jin Qiuchun, Tong Xiaoli, Ma Pengge and Bo Shukui (2013). “Iris Recognition by New Local Invariant Feature Descriptor”, Journal of Computational Information Systems. 9(5).
16. Kaur Gaganpreet, Girdhar Akshay, Kaur Manvjeet (2010). “Enhanced Iris Recognition System”, International Journal of Computer Applications (0975 – 8887).8(1).
17. Kekre.H.B.,Thepade Sudeep D., Jain Juhi and Agrawal Naman (2010). “Iris Recognition using Texture Features Extracted from Haarlet Pyramid”, International Journal of Computer Applications (0975 – 8887), 11(12).
18. Khan Mohd Basheer, Lavanya P. (2013). “Advanced Secured Vehicle System with Iris Technology and Auto Speed”, Advanced Secured Vehicle System with Iris Technology and Auto Speed. ISSN: 2321-9939. 19. Konduri Harsha and Samrat Sai (2012). “Canny Edge Detection”, Retrieved from www-student.cse.buffalo.edu/~sreehars/images/CANNY_REPORT.pdf. Accessed on May 24, 2015.
20. Li Ma, Tieniu Tan, Yunhong Wang and Dexin Zhang (2003). “Personal Identification Based on Iris Texture Analysis”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(12): 1519- 1533.
21. Maidstone Robert (2012). “Wavelets in a Two-Dimensional Context”, Retrieved from www.lancanster.ac.uk/pg/maidston/FinalReport.pdf on Aug 2014. Algorithm for grey image retrieved from http://www.mathworks.com/help/matlab/ref/rgb2grey.html, accessed 26 May 2015.
22. Monaheng Matsoso Samuel and Kuruba Padmaja (2013). “Iris Recognition Using Circular Hough Transform”, International Journal of Innovative Research in Science, Engineering and Technology;2(8).
23. Mythili C., Karitha V. (2011). “Efficient Technique for Colour Image Noise Reduction”, The Research Bulletin of Jordan ACM, 2(3): 41-44.
24. Nitasha, Sharma Shammi and Sharma Reecha (2012). “Comparison between Circular Hough Transform And Modified Canny Edge Detection Algorithm For Circle Detection”, International Journal of Engineering Research & Technology (IJERT); 1(3).
25. Patil C.M., Patilkulkarani and Sudarshan (2009). “An Approach of Iris Feature Extraction for Personal identification”, In Proceedings ofISCO at Coimbatore, pg. 43.
26. Rydgren Erik, Ea Thomas, Amiel Frédéric, Rossant Florence and Amara Amara(2004). “Iris Features Extraction Using Wavelet Packets”, Institut Supérieurd' Electronique de Paris, ISEP 21 rue d'Assas 75270 Paris Cedex 06.
27. Sifuzzaman M., Islam M. R., and Ali M. Z. (2009). “Application of Wavelet Transform and its Advantages Compared to Fourier Transform”, Journal of Physical Sciences. 13:121-134.
28. Tan T., Ma L., Wang Y. and Zhang D. (2003). “Personal Identification based on Iris Texture Analysis”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(12): 1519-1533.
29. Vatsa M., Singh R., and Noore A. (2005). “Reducing the False Rejection Rate of Iris Recognition Using Textural and Topological Features”, International Journal of Signal Processing 2. (ISSN: 1304-4494).
30. Wildes R. (1997). “Iris recognition: an emerging biometric technology”. Proceedings of the IEEE, 85(9):1348- 1363.
31. Wildes R., Asmuth J., Green G., Hsu S., Kolczynski R., Matey J. and McBride S. (2011). “A system for automated iris recognition”, Proceedings IEEE Workshop on Applications of Computer Vision, Sarasota, FL. Pp. 121-128.

Downloads

Published

2015-06-23

How to Cite

TundeAdeniyi, T., Olabode, O., Iwasokun, G. B., Oluwadare, S. A., & Akinyede, R. O. (2015). BIOMETRIC PERSONAL IDENTIFICATION ON 2D WAVELET TRANSFORM AND CHI-SQUARED MODEL. INTERNATIONAL JOURNAL OF COMPUTERS &Amp; TECHNOLOGY, 14(9), 6074–6084. https://doi.org/10.24297/ijct.v14i9.3984

Issue

Vol. 14 No. 9 (2015)

Section

Research Articles
Crossref
Scopus
Google Scholar
Europe PMC

Most read articles by the same author(s)