A Review of Iris Recognition Algorithms

Abdulrahman Aminu Ghali - Universiti Tun Hussein Onn Malaysia, Johor, Malaysia
Sapiee Jamel - Universiti Tun Hussein Onn Malaysia, Johor, Malaysia
Kamaruddin Malik Mohamad - Universiti Tun Hussein Onn Malaysia, Johor, Malaysia
Nasir Abubakar Yakub - Universiti Tun Hussein Onn Malaysia, Johor, Malaysia
Mustafa Mat Deris - Universiti Tun Hussein Onn Malaysia, Johor, Malaysia

Citation Format:

DOI: http://dx.doi.org/10.30630/joiv.1.4-2.62


With the prominent needs for security and reliable mode of identification in biometric system. Iris recognition has become reliable method for personal identification nowadays. The system has been used for years in many commercial and government applications that allow access control in places such as office, laboratory, armoury, automated teller machines (ATMs), and border control in airport. The aim of the paper is to review iris recognition algorithms. Iris recognition system consists of four main stages which are segmentation, normalization, feature extraction and matching. Based on the findings, the Hough transform, rubber sheet model, wavelet, Gabor filter, and hamming distance are the most common used algorithms in iris recognition stages.  This shows that, the algorithms have the potential and capability to enhanced iris recognition system. 


iris recognition; segmentation; normalization; feature extraction; matching.

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N. Othman, B. Dorizzi, and S. Garcia-Salicetti, OSIRIS: An open source iris recognition software, Pattern Recognition. Letters, vol. 82, pp. 124–131, 2016.

Y. A. Betancourt and M. G. Silvente, A keypoints-based feature extraction method for iris recognition under variable image quality conditions, Knowledge-Based Syst., vol. 92, pp. 169–182, 2015.

P. M. Patil, Iris Recognition in Less Constrained Environment, International Journal of Emerging Technology and Advanced Engineering vol. 3, no. 7, pp. 196–200, 2013.

A. A. Ghali, S. Jamel, Z. A. Pindar, A. H. Disina, and M. M. Deris, “Reducing Error Rates for Iris Image using higher Contrast in Normalization process,†IOP Conf. Ser. Mater. Sci. Eng., vol. 266, 2017.

J. R. A. Oluwakemi, J.S. Sadiku, A. kayode, ris Feature Extraction for Personal Identification using Fast Wavelet Transform ( FWT ), vol. 6, no. 9, pp. 1–6, mar, 2014.

L. Flom and A. Safir, Iris recognition system, US Patent, 4,641,349, 1987.

L. Ma, T. Tan, Y. Wang, and D. Zhang, Personal Identification Based on Iris Texture Analysis, vol. 25, no. 12, pp. 1519–1533, Dec , 2003.

T. Mansfield,, G. Kelly, D. Chandler, and J. Kane. Biometric product testing final report. Computing, National Physical Laboratory, (2001).

H. Rai, A. yada., Iris recognition using combined support vector machine and Hamming distance approach, no 2. pp 588-593 Feb, 2013.

N.Y. Tay, K. M. Mok, A review of iris recognition algorithms in information technology international symposium on vol. 2, pp. 1-7 Aug, 2008.

L. Masek, Recognition of Human Iris Patterns for Biometrics Identification, pp. 1-7, 2003.

W. K. Kong, D. Zhang, Accurate Iris Segmentation Based on Novel Reflection and Eyelash Detection Model, Intelligent Multimedia, Video and Speech Processing, International Symposium on. IEEE pp. 3–6, 2001.

P. Punyani, A. Kumar, and R. Gupta, An optimized Iris Recognition System using MOGA followed by Combined Classifiers, International Journal of Research in Advent Technology vol. 4, no. 3, pp. 221–226, 2016.

K. Okokpujie, E. Noma-osaghae, and S. John, An Improved Iris Segmentation Technique Using Circular Hough Transform, International Conference on Information Theoretic Security PP. 203-211, 2017.

J. G. Daugman, High confidence visual recognition of persons by a test of statistical independence. IEEE transactions on pattern analysis and machine intelligence. IEEE, vol.15 no. 11, pp. 1148-1161. 1993.

R. P. Wildes. J. C. Asmuth, G. L. Green, C.H. Stephen, R. J. Kolczynski, J. R. Matey, S. E. Mcbride, A System for Automated Iris Recognition, IEE pp. 121–128, Dec 1994.

W. W. Boles and B. Boashash, A Human Identification Technique Using Images of the Iris and Wavelet Transform, EEE transactions on signal processing vol. 46, no. 4, pp. 1185–1188, 1998.

E. M. Arvacheh, A Study of Segmentation and Normalization for Iris Recognition Systems , 2006.

T. Lefevre, B. Dorizzi, S. Garcia-salicetti, N. Lemperiere, and S. Belardi, “Effective elliptic fitting for iris normalization q,†Comput. Vis. Image Underst, , vol. 117, no. 6, pp. 732–745, 2013.

K. Nguyen, C. Fookes, R. Jillela, S. Sridharan, and A. Ross, Long range iris recognition , A survey, vol. 72, pp. 123–143, 2017.

M. M. Khaladkar and S. R. Ganorkar, “A Novel Approach for Iris Recognition,†vol. 1, no. 4, 2012.

M. N. Othman, Fusion techniques for iris recognition in degraded sequences, no.1, pp. 4–5, 2016.

R. Youmaran, Algorithms to process and measure biometric information content in low quality face and iris images. University of Ottawa 2011.

J. Daugman and C. Downing, Epigenetic randomness , complexity and singularity of human iris patterns, Proceedings of the Royal Society of London B: Biological Sciences, no. December 2000, pp. 1737–1740, 2001.