Identify a person from Iris Pattern using GLCM features and Machine Learning Techniques
|International Journal of Computer Science and Engineering|
|© 2020 by SSRG - IJCSE Journal|
|Volume 7 Issue 9|
|Year of Publication : 2020|
|Authors : Deepak Singh, Mr. Mohan Rao Mamdikar|
How to Cite?
Deepak Singh, Mr. Mohan Rao Mamdikar, "Identify a person from Iris Pattern using GLCM features and Machine Learning Techniques," SSRG International Journal of Computer Science and Engineering , vol. 7, no. 9, pp. 25-29, 2020. Crossref, https://doi.org/10.14445/23488387/IJCSE-V7I9P105
In today's era, when we see a pandemic like a corona, we can understand the need for nonimpact biometric feature matching techniques. Iris recognition is one of the accurate biometric features that can be used to identify a person. Iris recognition, as an emerging biometric recognition approach, is becoming an active topic in both research and practical applications; Iris recognition is recognizing an individual by analyzing the apparent pattern of his or her iris. A typical iris recognition system includes iris imaging, iris detection, feature extraction, and recognition. Here, we are proposing a straightforward approach for segmenting the iris patterns using a global thresholding technique. After this step, we get the iris' pupil, which is subtracted from the original image to urge the iris part. Then GLCM features are extracted from the iris, and machine learning techniques will do training and testing. We plan to perform Experiments using iris images obtained from the CASIA database. The general accuracy we get is promising, with near about 99% on using a support vector machine classifier.
Iris detection, machine learning techniques, CASIA dataset, GLCM features.
 Shaabad A.Sahmoud and Ibrahim S. Abuhaiba, “Efficient iris segmentation method in unconstrained environments,” Pattern Recognition, vol.46, no.12, pp. 3174-3185, 2013.
 John Daugman, “Statistical richness of visual phase information: update on recognizing persons by iris patterns,” International Journal of Computer Vision, vol. 45, no.1, pp.25-38, 2001.
 Leonard Flom and Aran Safir, “Iris recognition system,” U. S. Patent 4641349, 1987.
 Daugman J., “Recognizing persons by their iris patterns”, Proc. Advances in Biometric Person Authentication, Vol. 3338, pp. 5 - 25,2004.
 Daugman J., “The importance of being random: statistical principles of iris recognition”, Proc. Pattern Recognit., Vol. 36, pp. 279 – 291, 2003.
 Proenca H., “An iris recognition approach through structural pattern analysis methods”, Expert Systems, Vol. 27, No. 1, pp. 6 - 16, 2010.
 Yu Chen, “A high efficient biometrics approach for unconstrained iris segmentation and recognition”, Ph.D. Thesis, College of Engineering and Computing, Florida International University, 2010.
 M. A. Mohamed, M. A. Abou-El-Soud and M. M. Eid, "Automated Algorithm for Iris Detection and Code Generation," 2009.
 S. S. Mabruka, N. S. Sonawane and J. A., "Biometric System using Iris Recognition," International Journal of Innovative Technology and Exploring Engineering, vol. 2, no. 5, 2013.
 M. Pradhan, "Next Generation Secure Computing: Biometric in Secure E-transaction," International Journal of Advance Research in Computer Science and Management Studies, vol. 3, no. 4, pp. 473-489, 2015.
 Ajay Kumar, Tak-Shing Chan, Chun-Wei, Tan,”Human Identification from at-a-distance Face Images using Sparse Representation of Local Iris Features,”978-1-4673-0397- 2/12/$31.00@2012 IEEE.
 iris image dataset https://en.wikipedia.org/wiki/Iris_recognition seen on 12Jan 2020.
 Shirke, Swati D., and C. RajaBhushnam. "Review of IRIS recognition techniques." 2017 International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies (ICAMMAET). IEEE, 2017.
 Chun-Wei Tan,Ajay Kumar,” Accurate Iris Recognition at a Distance Using Stabilized Iris Encoding and Zernike Moments Phase Features”, IEEE Trans.on Image Processing,Vol-23,no 9,Sept. 2014.
 B. Jain, Dr.M.K.Gupta and Prof.JyotiBharti, "Efficient Iris recognition algorithm using Method of Moments," in International Journal of Artificial Intelligence & Applications, 2012.
 N. Puhan, N. Sudha, H. Xia and X. Jiang, "Iris recognition on edge maps," in IET Computer Vision, 2007.
 Liu, X., Bowyer, K., Flynn, P.: “Experiments with an improved iris segmentation algorithm”. In: IEEE Workshop on Automatic Identification Advanced Technologies (2005)
 Li, H., Sun, Z., Tan, T.: “Robust iris segmentation based on learned boundary detectors”. In: International Conference on Biometrics (2012)
 Uhl, A., Wild, P.: “Weighted adaptive hough and ellipsopolar transforms for realtime iris segmentation.” In: International Conference on Biometrics (2012)
 He, Z., Tan, T., Sun, Z., Qiu, X.: “Toward accurate and fast iris segmentation for iris biometrics.” IEEE TPAMI 31(9), 1670–1684 (2009)
 Liu, X., Li, P., Song, Q.: “Eyelid localization in iris images captured in less constrained environment. In: Tistarelli, M., Nixon, M.S. (eds.)” ICB 2009. LNCS, vol. 5558, pp. 1140–1149. Springer, Heidelberg (2009).
 Shakti Chourasiya, Suvrat Jain, "A Study Review On Supervised Machine Learning Algorithms" SSRG International Journal of Computer Science and Engineering 6.8 (2019): 16-20.