Home > Archive > 2020 > Volume 10 Number 3 (May 2020) >
IJMLC 2020 Vol.10(3): 482-489 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.3.961

Iris Biometrics for Secure Authentication

Abdullah Ali Alshehri, Tyler Daws, and Soundararajan Ezekiel

Abstract—Identification of individuals based on behavior or biology is known as biometrics. A recent and widely used development in the field of biometrics is iris recognition for identification. The average iris recognition algorithm requires taking an image of the iris, then testing and segmenting the image. This is commonly achieved through both statistical and classical techniques. Every method has strengths and weaknesses. In this proposal, we present a novel iris recognition approach. The objective of our study is to create a technique for accessing computers and computer networks. Due to widespread use of computers in many forms (desktop, handheld, etc.) our technique offers valuable security for information. This wavelet-based algorithm will be capable of enhancing the scanned image, reducing noise, and extracting important elements of the picture to check against data within a database. Additionally, our method may be generalized to a variety of applications including surveillance, e-commerce, ATM transactions, and others.

Index Terms—DCNN, biometrics, multi-resolution transform, wavelet, image fusion.

A. Alshehri is with the Electrical Engineering Department, King Abdulaziz University, Jeddah, KSA (e-mail: aaashehri@gmail.com).
S. Ezekiel and T. Daws are with the Computer Science Department, Indiana University of Pennsylvania, Indiana, PA USA.

[PDF]

Cite: Abdullah Ali Alshehri, Tyler Daws, and Soundararajan Ezekiel, "Iris Biometrics for Secure Authentication," International Journal of Machine Learning and Computing vol. 10, no. 3, pp. 482-489, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


Article Metrics