A New Partitioning Method in Frequency Analysis of the Retinal Images for Human Identification

DOI: 10.4236/jsip.2011.24039   PDF   HTML     4,532 Downloads   7,586 Views   Citations


Retinal image is one of the robust and accurate biometrics methods to recognize a person. In this article we present a new biometric identification system based on Fourier transform and angular partitioning of the spectrum. In this method, at first, the optical disc is localized using template matching technique and used for rotating the retinal image into the reference position. It compensates the rotation effects which might occur during the scanning process. Fourier transform coefficient and angular partitioning of these coefficients are used for the purpose of feature definition in our method. The extract features are rotation invariant and robust against noise. Finally we employ Euclidean distance for feature matching. The proposed algorithm was tested using 40 images from DRIVE database and experimental results showed the efficiency of the proposed algorithm for the identification of retinal images with noise and rotation.

Share and Cite:

M. Sabaghi, S. Hadianamrei, A. Zahedi and M. Lahiji, "A New Partitioning Method in Frequency Analysis of the Retinal Images for Human Identification," Journal of Signal and Information Processing, Vol. 2 No. 4, 2011, pp. 274-278. doi: 10.4236/jsip.2011.24039.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] T. Dunstone and N. Yager, “Biometric System and Data Analysis,” Springer, New York, 2008, pp. 529-548.
[2] S. Nanavati, M. Thieme and R. Nanavati, “Biometrics Identity Verification in a Networked World,” John Wiley & Sons, Inc., New York, 2002.
[3] H. Farzin, H. A. Moghaddam and M. S. Moin, “A Novel Retinal Identification System,” EURASIP Journal on Advances in Signal Processing, Vol. 2008, 2008, Article ID: 280635.
[4] M. Shahnazi, M. Pahlevanzadeh and M. Vafadoost, “Wavelet Based Retinal Recognition,” 9th International Symposium on Signal Processing and Its Applications (ISSPA), Sharjah, February 2007, pp. 1-4.
[5] H. Tabatabaee, A. Milani-Fard and H. Jafariani, “A Novel Human Identifier System Using Retina Image and Fuzzy Clustering Approach,” Proceedings of the 2nd IEEE International Conference on Information and Communication Technologies (ICTTA06), Damascus, April 2006, pp. 1031-1036.
[6] M. Ortega, C. Marino, M. G. Penedo, M. Blanco and F. Gonzalez, “Biometric Authentication Using Digital Retinal Images,” Proceedings of the 5th WSEAS International Conference on Applied Computer Science (ACOS06), Hangzhou, April 2006, pp. 422-427.
[7] Z. W. Xu, X. X. Guo, X. Y. Hu and X. Cheng, “The Blood Vessel Recognition of Ocular Fundus,” Proceedings of the 4th International Conference on Machine Learning and Cybernetics (ICMLC05), Guangzhou, August 2005, pp. 4493-4498.
[8] R. C. Gonzalez and R. E. Woods, “Digital Image Processing,” Pearson Education Inc., New Delhi, 2003, pp. 548-560.
[9] J. Staal, M. D. Abramoff, M. Niemeijer, M. A. Viergever and B. van Ginneken, “Ridge-Based Vessel Segmentation in Colorimages of the Retina,” IEEE Transactions on Medical Imaging, Vol. 23, No. 4, 2004, pp. 501-509. doi:10.1109/TMI.2004.825627
[10] A. Hoover, V. Kouznetsova and M. Goldbaum, “Locating Blood Vessels in Retinal Images by Piecewise Threshold Probing of a Matched Filter Response,” IEEE Transactions on Medical Imaging, Vol. 19, No. 3, 2000, pp. 203-210. doi:10.1109/42.845178

comments powered by Disqus

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.