Iris Data Compression Based on Hexa-Data Coding

Authors

  • Ghadah AL-Khafaji Department of Computer Science, College of Science, University of Baghdad, Baghdad, Iraq
  • Haider Hameed Al-Mahmood bDepartment of Computer Science, College of Science, Al-Mustansiriya University, Baghdad, Iraq
  • Mohammed M. Siddeq Department of Computer Engineering, North Technical University, Kirkuk, Iraq
  • Marcos. A. Rodrigues Geometric Modelling and Pattern Recognition Research Group (GMPR), Sheffield Hallam University

DOI:

https://doi.org/10.29304/jqcm.2023.15.2.1235

Keywords:

Iris segmentation, Lossless compression, Hexa compression

Abstract

Iris research is focused on developing techniques for identifying and locating relevant biometric features, accurate segmentation and efficient computation while lending themselves to compression methods. Most iris segmentation methods are based on complex modelling of traits and characteristics which, in turn, reduce the effectiveness of the system being used as a real time system. This paper introduces a novel parameterized technique for iris segmentation. The method is based on a number of steps starting from converting grayscale eye image to a bit plane representation, selection of the most significant bit planes followed by a parameterization of the iris location resulting in an accurate segmentation of the iris from the original image. A lossless Hexadata encoding method is then applied to the data, which is based on reducing each set of six data items to a single encoded value. The tested results achieved acceptable saving bytes performance for the 21 iris square images of sizes 256x256 pixels which is about 22.4 KB on average with 0.79 sec decompression  average time, with high saving bytes performance for 2 iris non-square images of sizes 640x480/2048x1536 that reached 76KB/2.2 sec, 1630 KB/4.71 sec respectively, Finally, the proposed promising techniques standard lossless JPEG2000 compression techniques with reduction about 1.2 and more in KB saving that implicitly demonstrating the power and efficiency of the suggested lossless biometric techniques.

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References

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Published

2023-09-24

How to Cite

AL-Khafaji, G., Al-Mahmood, H. H., Siddeq, M. M., & Rodrigues, M. A. (2023). Iris Data Compression Based on Hexa-Data Coding. Journal of Al-Qadisiyah for Computer Science and Mathematics, 15(2), Comp Page 95–113. https://doi.org/10.29304/jqcm.2023.15.2.1235

Issue

Section

Computer Articles