An Efficient Iris Image Thresholding based on Binarization Threshold in Black Hole Search Method

  • Authors

    • Muktar Danlami
    • Sofia Najwa Ramli
    • Nur Izzah Syahira Jemain
    • Zahraddeen Pindar
    • Sapiee Jamel
    • Mustafa Mat Deris
    2018-12-09
    https://doi.org/10.14419/ijet.v7i4.31.23337
  • Iri, , Image processing, Black Hole Search Method, Segmentation.

  • Abstract

    In iris recognition system, the segmentation stage is one of the most important stages where the iris is located and then further segmented into outer and lower boundary of iris region. Several algorithms have been proposed in order to segment the outer and lower boundary of the iris region. The aim of this research is to identify the suitable threshold value in order to locate the outer and lower boundaries using Black Hole Search Method. We chose these methods because of the ineffient features of the other methods in image indetification and verifications. The experiment was conducted using three data set; UBIRIS, CASIA and MMU because of their superiority over others. Given that different iris databases have different file formats and quality, the images used for this work are jpeg and bmp. Based on the experimentation, most suitable threshold values for identification of iris aboundaries for different iris databases have been identified. It is therefore compared with the other methods used by other researchers and found out that the values of 0.3, 0.4 and 0.1 for database UBIRIS, CASIA and MMU respectively are more accurate and comprehensive. The study concludes that threshold values vary depending on the database.

     

  • References

    1. [1] Sharma, Deepak, and Ashok Kumar. "Iris Recognition-An Effective Human Identification." International Journal of Computing and Business Research 2, no. 2 (2011): 1-12.

      [2] Prashar, Deepika. "A Close Approach to Iris Recognition System." IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661.

      [3] Surendar, A., Sadulla Shaik, and N. Usha Rani Rani. "Micro Sequence Identification of DNA Data Using Pattern Mining Techniques." Materials Today: Proceedings 5, no. 1 (2018): 578-587.

      [4] Singh, S., & Singh, K. “Segmentation techniques for Iris recognition systemâ€, International Journal of Scientific & Engineering Research, 2(4), (2011). 1-8.

      [5] Abidin, Z. Zainal, M. Manaf, A. S. Shibghatullah, SHA Mohd Yunus, S. Anawar, and Z. Ayop. "Iris segmentation analysis using integro-differential and hough transform in biometric system." Journal of Telecommunication, Electronic and Computer Engineering (JTEC) 4, no. 2 (2012): 41-48.

      [6] Mohammadi Arvacheh, Ehsan. "A study of segmentation and normalization for iris recognition systems." Master's thesis, University of Waterloo, 2006.

      [7] Masek, Libor. "Recognition of human iris patterns for biometric identification." (2003): 1-7.

      [8] Ng, Richard Yew Fatt, Yong Haur Tay, and Kai Ming Mok. "An effective segmentation method for iris recognition system." (2008): 548-553.

      [9] Singla, Sunil Kumar, and Parul Sethi. "Challenges at different stages of an iris based biometric system." Songklanakarin Journal of Science & Technology 34, no. 2 (2012).

      [10] Teo, C. C., & Ewe, H. T. “An efficient one-dimensional fractal analysis for iris recognitionâ€. (2005).

      [11] Firdousi, R., & Parveen, S. “Local Thresholding Techniques in Image Binarizationâ€, International Journal Of Engineering and Computer Science, 3(3), (2014). 4062-4065

      [12] Garg, N. “Binarization Techniques used for grey scale imagesâ€, International Journal of Computer Applications, 71(1). (2013).

      [13] Raju, P. D. R., & Neelima, G. “Image segmentation by using histogram thresholdingâ€. International Journal of Computer Science Engineering and Technology, 2(1), (2012). 776-779.

      [14] Senthilkumaran, N., & Vaithegi, S. “Image segmentation by using thresholding techniques for medical imagesâ€. Computer Science & Engineering: An International Journal, 6(1), (2016). 1-13.

      [15] Gonzalez and Richard E. Woods, “Digital Image Processingâ€, 3rd Edition, Pearson Education International. (2008).

      [16] Raid, A. M., Khedr, W. M., El-Dosuky, M. A., & Ahmed, W., “Jpeg Image Compression Using Discrete Cosine Transform- A Surveyâ€, International Journal of Computer Science & Engineering Survey (IJCSES), 5(2), (2014). 39-47.

      [17] Alarabeyyat, A., Al-Hashemi, S., Khdour, T., Btoush, M. H., Bani-Ahmad, S., Al-Hashemi, R., & Bani-Ahmad, S. “Lossless image compression technique using combination methodsâ€, Journal of Software Engineering and Applications, 5(10), (2012). 752-763

      [18] Khan, M. W. “A survey: Image segmentation techniquesâ€, International Journal of Future Computer and Communication, 3(2), (2014). 89-93.

      [19] Proença, H., & Alexandre, L. A. “UBIRIS: A noisy iris image databaseâ€, International Conference on Image Analysis and Processing LNCS 3617, 970–977. (2005). pp. 970-977.

      [20] Khobragade, K., & Kale, K. V. “A New Technique for Fast and Accurate Iris Localizationâ€, International Journal of Innovations in Engineering and Technology (IJIET), ISSN, 2319-1058. (2014).

      [21] Shanthi, R., & Dinesh, B. “Iris Based Authentication Systemâ€, IOSR Journal of Engineering, 3(4), (2013). 15-20.

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  • How to Cite

    Danlami, M., Najwa Ramli, S., Izzah Syahira Jemain, N., Pindar, Z., Jamel, S., & Mat Deris, M. (2018). An Efficient Iris Image Thresholding based on Binarization Threshold in Black Hole Search Method. International Journal of Engineering & Technology, 7(4.31), 34-39. https://doi.org/10.14419/ijet.v7i4.31.23337

    Received date: 2018-12-07

    Accepted date: 2018-12-07

    Published date: 2018-12-09