Iris Identification Based on the Fusion of Multiple Methods

  • Asaad Noori Hashim Department of Computer Science, Faculty of Computer Science and Mathematics, University of Kufa, Najaf, Iraq
  • Roaa Razaq Al-Khalidy Department of Computer Science, Faculty of Computer Science and Mathematics, University of Kufa, Najaf, Iraq
Keywords: Iris Identification, Scale Invariant Features Transform(SIFT), Local Binary Pattern (LBP), Difference of Gaussian (DoG)

Abstract

Iris recognition occupies an important rank among the biometric types of approaches as a result of its accuracy and efficiency. The aim of this paper is to suggest a developed system for iris identification based on the fusion of scale invariant feature transforms (SIFT) along with local binary patterns of features extraction. Several steps have been applied. Firstly, any image type was converted to  grayscale. Secondly, localization of the iris was achieved using circular Hough transform. Thirdly, the normalization to convert the polar value to Cartesian using Daugman’s rubber sheet models, followed by histogram equalization to enhance the iris region. Finally, the features were extracted by utilizing the scale invariant feature transformation and local binary pattern. Some sigma and threshold values were used for feature extraction, which achieved the highest rate of recognition. The programming was implemented by using MATLAB 2013. The matching was performed by applying the city block distance. The iris recognition system was built with the use of iris images for 30 individuals in the CASIA v4. 0 database. Every individual has 20 captures for left and right, with a total of 600 pictures. The main findings showed that the values of recognition rates in the proposed system are 98.67% for left eyes and 96.66% for right eyes, among thirty subjects.

Published
2021-04-30
How to Cite
Hashim, A. N., & Al-Khalidy, R. R. (2021). Iris Identification Based on the Fusion of Multiple Methods. Iraqi Journal of Science, 62(4), 1364-1375. https://doi.org/10.24996/ijs.2021.62.4.32
Section
Computer Science