The International Arab Journal of Information Technology (IAJIT)

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An Improved Iris Localization Method

Meisen Pan, Qi Xiong,

Iris research has become an inevitable trend in the application of identity recognition due to its uniqueness, stability, non-aggression and other advantages. In this paper, an improved iris localization method is presented. When the iris inner boundary is located, a method for extracting the iris inner boundary based on morphology operations with multi- structural elements is proposed. Firstly, the iris image is pre-processed, and then the circular connected region in the pre- processed image is determined, the parameters of the circular connected region is extracted, finally the center and the radius of the circular connected region is obtained, i.e., the iris inner boundary is excavated. When the iris outer boundary is located, a method for locating iris outer boundary based on annular region and improved Hough transform is proposed. The iris image is first filtered, and then the filtered image is reduced and an annular region is intercepted, finally Hough transform is used to search the circle within the annular region, i.e., the center and the radius of the iris outer boundary is obtained. The experimental results show that the location accuracy rate of this proposed method is at least 95% and the average running time is increased by 46.2% even higher. Therefore, this proposed method has the advantages of high speed, high accuracy, strong robustness and practicability.

[1] Alvarez-Betancourt Y. and Garcia-Silvente M., “A Key Points-Based Feature Extraction Method for Iris Recognition Under Variable Image Quality Conditions,” Knowledge Based Systems, vol. 92, no. (C), pp. 169-182, 2016.

[2] Arunachalamand M. and Amuthan K., “Finger Knuckle Print Recognition using MMDA with Fuzzy Vault,” International Arab Journal of Information Technology, vol. 17, no. 4, pp. 554- 561, 2020.

[3] Boles W., “A Security System Based on Human Iris Identification Using Wavelet Transform,” in Proceedings of the 1st International Conference on Knowledge-Based Intelligent Electronic Systems, Adelaide, pp. 533-541, 1997.

[4] Bowyer K., Hollingsworth K., and Flynn P., “Image Understanding for Iris Biometrics: A Survey,” Computer Vision and Image Understanding, vol. 110, no. 2, pp. 281-307, 2008.

[5] Chang Y., Shih T., Li Y., and Kumara W., “Effectiveness Evaluation of Iris Segmentation By Using Geodesic Active Contour (GAC),” The Journal of Supercomputing, vol. 76, no. 3, pp. 1628-1641, 2020.

[6] Daugman J., “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.

[7] Daugman J., “The Importance of Being Random: Statistical Principles of Iris Recognition,” Pattern Recognition, vol. 36, no. 2, pp. 279-291, 2003.

[8] Doan H. and Nguyen D., “A Method for Finding the Appropriate Number of Clusters,” The International Arab Journal of Information Technology, vol. 15, no. 4, pp. 675-682, 2018.

[9] Efimov Y., Leonov V., Odinokikh G., Solomatin I., “Finding the Iris Using Convolutional Neural Networks,” Journal of Computer and Systems Sciences International, vol. 60, no. 1, pp.108-117, 2021.

[10] Jan F., Min-Allah N., Agha S., Usman I., and Khan I., “A Robust Iris Localization Scheme for The Iris Recognition,” Multimedia Tools and Applications, vol. 80, no. 18, pp. 4579-4605, 2021.

[11] Kadir K., Gao H., Payne A., Soraghan J., and Berry C., “LV Wall Segmentation Using The Variational Level Set Method (LSM) with Additional Shape Constraint for Oedema Quantification,” Physics in Medicine and Biology, vol. 57, no. 19, pp. 6007-6023, 2012.

[12] Li P., Liu X., Xiao L., and Song Q., “Robust and Accurate Iris Segmentation in Very Noisy Iris Images,” Image and Vision Computing, vol. 28, no. 2, pp. 246-253, 2010.

[13] Lin Y., Hsieh T., Huang J., Yang C., Shen V., and Bui H., “Fast Iris Localization Using Haar- Like Features and Adaboost Algorithm,” Multimedia Tools and Applications, vol. 79, no. 12, pp. 34339-34362, 2020.

[14] Ma L., Tan T., Wang Y., and Zhang D., “Efficient Iris Recognition by Characterizing Key Local Variations,” IEEE Transactions Image Process, vol. 13, no. 6, pp. 739-750, 2004.

[15] Schuckers S., Schmid N., Abhyankar A., Dorairaj V., Boyce C., and Hornak L., “On Techniques for Angle Compensation in Nonideal Iris Recognition,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 37, no. 5, pp. 1176-1190, 2007.

[16] Sudha N., Puhan N., Xia H., and Jiang X., “Iris Recognition on Edge Maps,” IET Computer Vision, vol. 3, no. 1, pp. 1-7, 2009.

[17] Tan T., He Z., and Sun Z., “Efficient and Robust Segmentation of Noisy Iris Images for Non- Cooperative Iris Recognition,” Image and Vision Computing, vol. 28, no. 2, pp. 223-230, 2010.

[18] Zheng Z., Yang J., and Yang L., “A Robust Method for Eye Features Extraction on Color Image,” Pattern Recognition Letters, vol. 26, no. 14, pp. 2252-2261, 2005.