Review of human iris identification methods

Authors: Kandrina P.I.
Published in issue: #3(44)/2020
DOI: 10.18698/2541-8009-2020-3-587
Category: Informatics, Computer Engineering and Control | Chapter: Methods and Systems of Information Protection, Information Security
Keywords: mobile robot-nurse, vibration load, simulation, software, automated modeling, dynamics of a system of bodies, model of tire-road interaction, information protection, iris, biometric system, methods for identifying a person, intensity fluctuation, integral and differential operator, analysis of independent components, texture analysis
Published: 23.03.2020

Currently, the problem of protecting information from unauthorized access is especially relevant. One of the most reliable biometric technologies is iris identification technology. The article discusses the main stages of solving the problem of identifying a person by the iris, provides a list of image databases of the iris. A brief overview of existing methods for iris identification based on an integral and differential operator, texture analysis, intensity fluctuations and analysis of independent components is presented, the accuracy of methods for identifying a person by iris is analyzed.

References

[1] Johnston R. Can iris patterns be used to identify people? Chemical and Laser Sciences Division Annual Report LA-12331-PR. Los Alamos National Laboratory, 1992, pp. 81–86.

[2] Bowyer K.W., Hollingsworth K., Flynn P.J. Image understanding for iris biometrics: a survey, computer vision and image understanding. CVIU, 2008, vol. 10, no. 2, pp. 281–307. DOI: https://doi.org/10.1016/j.cviu.2007.08.005

[3] Proenca H., Alexandre L.A. UBIRIS: a noisy iris image database. ICIAP, 2005, pp. 970–977. DOI: https://doi.org/10.1007/11553595_119

[4] CASIA Iris image databases. cbsr.ia.ac.cn: website. URL: http://www.cbsr.ia.ac.cn/IrisDatabase.htm (accessed: 15.10.2019).

[5] Li M., Tan T., Wang Y., et al. Personal identification based on iris texture analysis. IEEE Trans. Pattern Anal. Mach. Intell., 2003, vol. 25, no. 12, pp. 1519–1533. DOI: https://doi.org/10.1109/TPAMI.2003.1251145

[6] Daugman J. How iris recognition works. IEEE Trans. Circuits Syst. Video Technol., 2004, vol. 14, no. 1, pp. 21–30. DOI: https://doi.org/10.1109/TCSVT.2003.818350

[7] Daugman J. High confidence visual recognition by a test of statistical independence. IEEE Trans. Pattern Anal. Mach. Intell., 1993, vol. 15, no. 11, pp. 1148–1161. DOI: https://doi.org/10.1109/34.244676

[8] Wildes R., Asmuth J., Green G., et al. A machine-vision system for iris recognition. Machine Vis. Apps., 1996, vol. 9, no. 1, pp. 1–8. DOI: https://doi.org/10.1007/BF01246633

[9] Wildes R. Iris recognition: an emerging biometric technology. Proc. IEEE, 1997, vol. 85, no. 9, pp. 1348–1363. DOI: https://doi.org/10.1109/5.628669

[10] Wildes R.P., Asmuth J.C., Green G.L., et al. A system for automated iris recognition. Proc. 2nd IEEE Workshop on Applications of Computer Vision, 1994, pp. 121–128. DOI: https://doi.org/10.1109/ACV.1994.341298

[11] Boles W.W., Boashash B. A human identification technique using images of the iris and wavelet transform. IEEE Trans. Signal Process., 1998, vol. 46, no. 4, pp. 1185–1188. DOI: https://doi.org/10.1109/78.668573

[12] Li M., Tan T., Wang Y., et al. Efficient Iris Recognition by characterizing key local variations. IEEE Trans. Image Process., 2004, vol. 13, no. 6, pp. 739–750. DOI: https://doi.org/10.1109/TIP.2004.827237

[13] Jong G.K., Youn H.G., Jang H.Y., et al. Method of iris recognition using cumulative-sum-based change point analysis and apparatus using the same. Patent US 20070014438. Appl. 18.04.2006, publ. 18.01.2007.

[14] Huang Y.-P., Luo X.W., Chen E.Y. An efficient iris recognition system. Proc. Int. Conf. Machine Learning and Cybernetics, 2002, pp. 450–454. DOI: https://doi.org/10.1109/ICMLC.2002.1176794

[15] Bodade R.M., Talbar S.N. Iris analysis for biometric recognition systems. Springer, 2014.

[16] Yang J., Poh N. Recent application in biometrics. InTech, 2011.