A Survey of Iris Recognition System

Authors

  • Ayu Fitrie Haziqah Sallehuddin School of Computer and Communication Engineering, Universiti Malaysia Perlis, Kampus Pauh Putra, 02600, Perlis
  • Muhammad Imran Ahmad School of Computer and Communication Engineering, Universiti Malaysia Perlis, Kampus Pauh Putra, 02600, Perlis
  • Ruzelita Ngadiran School of Computer and Communication Engineering, Universiti Malaysia Perlis, Kampus Pauh Putra, 02600, Perlis
  • Mohd Nazrin Md Isa School of Microelectronic Engineering, Universiti Malaysia Perlis, Kampus Pauh Putra, 02600, Perlis

Keywords:

Biometric System, Iris Recognition, Information Fusion,

Abstract

The uniqueness of iris texture makes it one of the reliable physiological biometric traits compare to the other biometric traits. In this paper, we investigate a different level of fusion approach in iris image. Although, a number of iris recognition methods has been proposed in recent years, however most of them focus on the feature extraction and classification method. Less number of method focuses on the information fusion of iris images. Fusion is believed to produce a better discrimination power in the feature space, thus we conduct an analysis to investigate which fusion level is able to produce the best result for iris recognition system. Experimental analysis using CASIA dataset shows feature level fusion produce 99% recognition accuracy. The verification analysis shows the best result is GAR = 95% at the FRR = 0.1%

References

Chou, C.-T., et al., Non-orthogonal view iris recognition system. IEEE Transactions on Circuits and Systems for Video Technology, 2010. 20(3): p. 417-429.

B.Raja, K., et al., Smartphone based visible iris recognition using deep sparse filtering. Pattern Recognition Letter, 2015. 57: p. 33-42.

Raghavendra, R. and A. Ross, Segmenting iris image in the visible spectrum with application in mobile biometrics. Pattern Recognition Letters, 2015. 57: p. 4-16.

Tsai, C.-C., et al., Iris recognition using possibilistic fuzzy matching on local features. IEEE Transactions on Systems, Man, and CyberneticsPart B: Cybernetics, 2012. 42(NO.1): p. 150-162.

Rahulkar, A.D. and R.S. Holambe, Partial iris feature extraction and recognition based on a new combined directional and rotated directional wavelet filter banks. Neurocomputing, 2012. 81: p. 12-23.

Szewczyk, R., et al., A reliable iris recognition algorithm based on reverse biorthogonal wavelet transform. Pattern Recognition Letter, 2012. 33: p. 1019-1026.

Raffei, A.F.M., et al., Feature extraction for different distances of visible reflection iris using multiscale sparse representation of local radon transform. Pattern Recognition, 2013. 46: p. 2622-2633.

Nabti, M., L. Ghouti, and A. Bouridane, An effective and fast iris recognition system based on a combined multiscale feature extraction technique. Pattern Recognition 2008. 41: p. 868-879.

Fairhurst, M. and M. Erbilek, Analysis of physical ageing effects in iris biometrics. IET Computer Vision, 2011. 5(6): p. 358-366.

Farouk, R.M., R. Kumar, and K.A. Riad, Iris matching using multidimensional artificial neural network. IET Computer Vision, 2011. 5(3): p. 178-184.

Sudha, N., et al., Iris recognition on edge maps. IET Computer Vision, 2009. 3(1): p. 1-7.

Umer, S., B.C. Dhara, and B. Chanda, Iris recognition using multiscale morphologic features. Pattern Recognition Letter, 2015. 65: p. 67 - 74.

Sim, H.M., et al., Multimodal biometrics: weighted score level fusion based on non-ideal iris and face images. Expert Systems With Applications, 2014. 41: p. 5390-5404.

Tallapragada, V.V.S. and E.G. Rajan, Improved kernel-based iris recognition system in the framework of support vector machine and hidden markov model. IET Image Process, 2012. 6(6): p. 661-667.

Abate, A.F., et al., BIRD: Watershed based iris detection for mobile devices. Pattern Recognition Letters, 2015. 57: p. 43-51.

R.Raghavendra and C. Busch, Robust scheme for iris presentation attack detection using multiscale binarized statistical image features.IEEE Transactions on Information Forensics and Security, 2015. 10(4): p. 703-715.

Kong, A.W.-K., Member, and IEEE, A statistical analysis of IrisCode and its security implications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015. 37(3): p. 513-528.

Proenca, H., S. Member, and IEEE, Iris recognition: what is beyond bit fragility? IEEE Transactions on Information Forensics and Security, 2015. 10(2): p. 321-332.

Raffei, A.F.M., et al., Frame detection using gradients fuzzy logic and morphological processing for distant colour eye images in an intelligent iris recognition system Applied Soft Computing, 2015. 37: p. 363-381.

Smereka, J.M., et al., Probabilistic deformation models for challenging periocular image verification. IEEE Transactions on Information Forensics and Security, 2015. 10(9): p. 1875-1890.

Proenca, H., S. Member, and IEEE, Ocular biometrics by score-level

fusion of disparate experts. IEEE Transactions on Image Processing,

23(12): p. 5082-5093.

Park, U., et al., Periocular biometrics in the visible spectrum. IEEE

Transactions On Information Forensics And Security, 2011. 6(1): p. 96-106.

. Park, H.-A. and K.R. Park, Iris recognition based on score level fusion

by using SVM. Pattern Recognition Letters, 2007. 28: p. 2019-2028.

Vatsa, M., et al., Improving iris recognition performance using

segmentation, quality enhancement, match score fusion, and indexing.

IEEE Transactions On Systems, Man, and Cybernetics-Part B:

Cybernetics, 2008. 38(4): p. 1021-1035.

Vatsa, M., R. Singh, and A. Noore, Reducing the false rejection rate of

iris recognition using textural and topological features. Int. J. Signal

Process, 2005. 2(1): p. 66-72.

Bishnu, A., et al., Euler vector for search and retrieval of gray-tone

images. IEEE Transactions On Systems, Man, and Cybernetics- Part B:

Cybernetics, 2005. 35(4): p. 801-812.

Song, Y., W. Cao, and Z. He, Robust iris recognition using sparse error correction model and discriminative dictionary learning.

Neurocomputing, 2014. 137: p. 198-204.

Zhu, Y., T. Tan, and Y. Wang, Biometric personal identification based on iris patterns. Proc. 15th Int. Confr. on Pattern Recognition (ICPR), 2000. 2: p. 2801.

J., D., How iris recognition works. IEEE Transactions on Circuits and Systems for Video Technology, 2004. 14(1): p. 21-30.

Riad, K.A., R.M. Farouk, and I.A. Othman, Time of matching reduction and improvement of sub-optimal image segmentation for iris recognition. OSDA-2010, 2010: p. 49-66.

Othman, N. and B. Dorizzi, Impact of quality-based fusion techniques for video-based iris recognition at a distance. IEEE Transactions on Information Forensics and Security, 2015. 10(8): p. 1590-1602.

Barra, S., et al., Ubiquitous iris recognition by means of mobile devices. Pattern Recognition Letters, 2015. 57: p. 66-73.

Guru, D.S., M.G. Suraj, and S. Manjunath, Fusion of covariance matrices of PCA and FLD. Pattern Recognition Letters 2011. 32: p. 432-440.

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Published

2016-07-01

How to Cite

Sallehuddin, A. F. H., Ahmad, M. I., Ngadiran, R., & Md Isa, M. N. (2016). A Survey of Iris Recognition System. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 8(4), 133–138. Retrieved from https://jtec.utem.edu.my/jtec/article/view/1188

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