Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31203
Iris Recognition Based On the Low Order Norms of Gradient Components

Authors: Iman A. Saad, Loay E. George


Iris pattern is an important biological feature of human body; it becomes very hot topic in both research and practical applications. In this paper, an algorithm is proposed for iris recognition and a simple, efficient and fast method is introduced to extract a set of discriminatory features using first order gradient operator applied on grayscale images. The gradient based features are robust, up to certain extents, against the variations may occur in contrast or brightness of iris image samples; the variations are mostly occur due lightening differences and camera changes. At first, the iris region is located, after that it is remapped to a rectangular area of size 360x60 pixels. Also, a new method is proposed for detecting eyelash and eyelid points; it depends on making image statistical analysis, to mark the eyelash and eyelid as a noise points. In order to cover the features localization (variation), the rectangular iris image is partitioned into N overlapped sub-images (blocks); then from each block a set of different average directional gradient densities values is calculated to be used as texture features vector. The applied gradient operators are taken along the horizontal, vertical and diagonal directions. The low order norms of gradient components were used to establish the feature vector. Euclidean distance based classifier was used as a matching metric for determining the degree of similarity between the features vector extracted from the tested iris image and template features vectors stored in the database. Experimental tests were performed using 2639 iris images from CASIA V4-Interival database, the attained recognition accuracy has reached up to 99.92%.

Keywords: iris recognition, contrast stretching, gradient features, texture features, Euclidean metric

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1720


[1] J. W. Lewis, "Biometrics for Secure Identity Verification: Trends and Developments", M.Sc. Thesis, University of Maryland, Bowie State University, 2002. http://acsupport/ /lewis.pdf
[2] J. Daugman, "The Importance of Being Random: Statistical Principles of Iris Recognition", Pattern Recognition Vol. 36, Pp. 279-291, 2003.
[3] L. Ma, T. Tan, Y. Wang, and D. Zhang, "Personal Identification Based on Iris Texture Analysis", IEEE Trans. Pattern Analysis and Machine intelligence, Vol.25, No. 12, Pp. 1519-1533, 2003.
[4] R. Wildes, "Iris Recognition: An emerging biometric technology,” Proc. IEEE, Vol. 85, Pp. 1348-1363, 1997.
[5] W. Boles, B. Boashah, "A Human Identification Technique Using Images of the Iris and Wavelet Transform,” IEEE Trans. on Signal Processing, Vol. 46, No. 4, Pp. 1185-1188, 1998.
[6] J. G. Daugman, "High Confidence Visual Recognition of Persons by a Test of Statistical Independence", IEEE Tans. Pattern Analysis and Machine Intelligence, Vol. 15, No. 11, Pp. 1148-1161, 1993.
[7] D. Monro, R. Soumyadip and Z. Dexin, "DCT-based Iris Recognition", IEEE. Trans. Patt. Anal. Mach. Intell., Vol. 29, Pp. 586-595. DOI:10.1109/TPAMI.2007.1002, 2007.
[8] A. Azizi and H. R. Pourreza, "Efficient IRIS Recognition through Improvement of Feature Extraction and Subset Selection", International Journal of Computer Science and Information Security (IJCSIS), Vol. 2, No.1, 2009.
[9] S. Lim, K. Lee, O. Byeon and T. Kim, "Efficient Iris Recognition through Improvement of Feature Vector and Classifier", ETRIJ, Vol.23, No.2, Pp. 61–70, 2001.
[10] A. M. Sarhan, "Iris Recognition Using Discrete Cosine Transform and Artificial Neural Networks,” Journal of Computer Science vol. 5 (5), Pp. 369-373, 2009.
[11] N. Najafi and S. Ghofrani, "Iris Recognition Based on Using Ridgelet and Curvelet Transform", Int. J. Signal Process Image Process Pattern Recognition, Vol.4, No.2, Pp. 7–18, 2011.
[12] S.J. Sheeba, S.S. Jeya, S. Veluchamy, "Security System Based on Iris Recognition", Research Journal of Engineering Sciences, Vol. 2, No. 3, Pp. 16-21, 2013.
[13] I. A. Saad and L. E. George, "Iris Recognition Based on the Spatial Density Distribution of the Gradient Components,” unpublished.
[14] I. A. Saad and L. E. George, "Robust and Fast Iris Localization Using Contrast Stretching and Leading Edge Detection", International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), Vol. 3, Pp. 61-67, 2014.
[15] M. G. Thomason and R. C. Gonzalez, "Syntactic Pattern Recognition", Publisher: Westview Pressxix, 1978