Extrema Points Application in Determining Iris Region of Interest

Authors

  • Zuraini Othman Department of Intelligent Computing and Analytics, Faculty of information & Communication Technology, Universiti Teknikal Malaysia Melaka
  • Azizi Abdullah Center for Artificial Intelligence Technology, Faculty of information Science and Technology, Universiti Kebangsaan Malaysia Bangi, Selangor
  • Sharifah Sakinah Syed Ahmad Department of Intelligent Computing and Analytics, Faculty of Information and Communications Technology, Universiti Teknikal Malaysia Melaka
  • Fauziah Kasmin Department of Intelligent Computing and Analytics, Faculty of Information and Communications Technology, Universiti Teknikal Malaysia Melaka

DOI:

https://doi.org/10.37134/ejsmt.vol6.1.5.2019

Keywords:

Extrema points, iris recognition system, region of interest, second derivative model

Abstract

Extrema points are usually applied to solve everyday problems, for example, to determine the potential of a created tool and for optimisation. In this study, extrema points were used to help determine the region of interest (ROI) for the iris in iris recognition systems. Iris recognition is an automated method of biometric identification that uses mathematical pattern-recognition techniques on the images of one or both irises of an individual's eyes, where the complex patterns are unique, stable, and can be seen from a distance. In order to obtain accurate results, the iris must be localised correctly. Hence, to address this issue, this paper proposed a method of iris localisation in the case of ideal and non-ideal iris images. In this study, the algorithm was based on finding the classification for the region of interest (ROI) with the help of a Support Vector Machine (SVM) by applying a histogram of grey level values as a descriptor in each region from the region growing technique. The valid ROI was found from the probabilities graph of the SVM obtained by looking at the global minimum conditions determined by a second derivative model in a graph of functions. Furthermore, the model from the global minimum condition values was used in the test phase, and the results showed that the ROI image obtained helped in the elimination of sensitive noise with the involvement of fewer computations, while reserving relevant information.

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References

Feng, H., & Wah, C. C. (2003). Online signature verification using a new extreme points warping technique. Pattern Recognition Letters, 24(16), 2943–2951. https://doi.org/10.1016/S0167-8655(03)00155-7

Gupta, G. K., & Joyce, R. C. (2007). Using position extrema points to capture shape in on-line handwritten signature verification. Pattern Recognition, 40(10), 2811–2817. https://doi.org/10.1016/j.patcog.2007.01.014

Lu, Y., & Lu, R. (2018). Fast Bi-dimensional empirical mode decomposition as an image enhancement technique for fruit defect detection. Computers and Electronics in Agriculture, 152(June), 314–323. https://doi.org/10.1016/j.compag.2018.07.025

Usman, M., & Usman, M. (2018). Spatial frequency based video stream analysis for object classification and recognition in clouds Spatial Frequency based Video Stream Analysis for Object Classification and Recognition in Clouds

Yang, M. D., Huang, K. S., Yang, Y. F., Lu, L. Y., Feng, Z. Y., & Tsai, H. P. (2016). Hyperspectral Image Classification Using Fast and Adaptive Bidimensional Empirical Mode Decomposition With Minimum Noise Fraction. IEEE Geoscience and Remote Sensing Letters, PP(99), 1–5. https://doi.org/10.1109/LGRS.2016.2618930

Abiyev, R. H., & Altunkaya, K. (2009). Neural network based biometric personal identification with fast iris segmentation. International Journal of Control, Automation and Systems, 7(1), 17–23. https://doi.org/10.1007/s12555-009-0103-1

Jayalakshmi, S., & Sundaresan, M. (2013). A survey on Iris Segmentation methods. In 2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering (pp. 418–423). Ieee. https://doi.org/10.1109/ICPRIME.2013.6496513

Negin, M., Chmielewski, T. a., Salganicoff, M., Camus, T. a., Cahn Von Seelen, U. M., Venetianer, P. L., & Zhang, G. G. (2000). Iris biometric system for public and personal use. Computer, 33(2), 70–75. https://doi.org/10.1109/2.820042

Lodin, A., & Demea, S. (2009). Design of an iris-based medical diagnosis system. 2009 International Symposium on Signals, Circuits and Systems, ISSCS 2009, 3–6. https://doi.org/10.1109/ISSCS.2009.5206187

Ma, L., Tan, T., Wang, Y., & Zhang, D. (2003). Personal identification based on iris texture analysis. Pattern Analysis and Machine Intelligence, IEEE Transactions On, 25(12), 1519–1533. https://doi.org/10.1109/TPAMI.2003.1251145

Sankowski, W., Grabowski, K., Napieralska, M., Zubert, M., & Napieralski, A. (2010). Reliable algorithm for iris segmentation in eye image. Image and Vision Computing, 28(2), 231–237. https://doi.org/10.1016/j.imavis.2009.05.014

Li, Y., Li, W., & Ma, Y. (2012). Accurate iris location based on region of interest. Proceedings - 2012 International Conference on Biomedical Engineering and Biotechnology, ICBEB 2012, 704–707. https://doi.org/10.1109/iCBEB.2012.47

Othman, Z., & Abdullah, A. (2018). Adaptive Threshold and Piecewise Fitting for Iris Localisation. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(2–7), 153–158

Chapra, S. C., & Canale, R. P. (2005). Numerical methods for engineers (Vol. 5th). https://doi.org/10.1016/0378-4754(91)90127-O

Strang, G. (1991). CALCULUS. Wellesley: Wellesley Cambridge Press

Abdou, I. E., & Pratt, W. K. (1979). Quantitative Design and Evaluation of Enhancement/Thresholding Edge Detectors. Proc IEEE, 67(5), 753–763. https://doi.org/10.1109/PROC.1979.11325

Ibrahim, M. T., Khan, T. M., Khan, S. a., Khan, M. A., & Guan, L. (2012). Iris localization using local histogram and other image statistics. Optics and Lasers in Engineering, 50(5), 645–654. https://doi.org/10.1016/j.optlaseng.2011.11.008

Abdullah, A., Veltkamp, R. C., & Wiering, M. a. (2009). Spatial pyramids and two-layer stacking SVM classifiers for image categorization: A comparative study. Proceedings of the International Joint Conference on Neural Networks, 5–12. https://doi.org/10.1109/IJCNN.2009.5178743

Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007/BF00994018

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Published

2019-06-26

How to Cite

Othman, Z., Abdullah, A., Syed Ahmad, S. S., & Kasmin, F. (2019). Extrema Points Application in Determining Iris Region of Interest. EDUCATUM Journal of Science, Mathematics and Technology, 6(1), 35–40. https://doi.org/10.37134/ejsmt.vol6.1.5.2019