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Reseach Article

Reanalyzing Li and Tao. (2014): Investigating Algorithm Recognition on Dark Irises

by Adebayo Omotosho, Omotanwa Adegbola, Michael Edobor
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 155 - Number 10
Year of Publication: 2016
Authors: Adebayo Omotosho, Omotanwa Adegbola, Michael Edobor

Adebayo Omotosho, Omotanwa Adegbola, Michael Edobor . Reanalyzing Li and Tao. (2014): Investigating Algorithm Recognition on Dark Irises. International Journal of Computer Applications. 155, 10 ( Dec 2016), 44-52. DOI=10.5120/ijca2016912460

@article{ 10.5120/ijca2016912460,
author = { Adebayo Omotosho, Omotanwa Adegbola, Michael Edobor },
title = { Reanalyzing Li and Tao. (2014): Investigating Algorithm Recognition on Dark Irises },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2016 },
volume = { 155 },
number = { 10 },
month = { Dec },
year = { 2016 },
issn = { 0975-8887 },
pages = { 44-52 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2016912460 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T00:00:57.475973+05:30
%A Adebayo Omotosho
%A Omotanwa Adegbola
%A Michael Edobor
%T Reanalyzing Li and Tao. (2014): Investigating Algorithm Recognition on Dark Irises
%J International Journal of Computer Applications
%@ 0975-8887
%V 155
%N 10
%P 44-52
%D 2016
%I Foundation of Computer Science (FCS), NY, USA

Iris recognition algorithms have been proposed in several works with some of these algorithms solving mainly templates identification accuracy issues. The need to test these algorithms for identification or matching speed cannot be over-emphasized as this is also important when deploying algorithms in real application. This aim of this paper is to implement and validate a selected iris recognition algorithm. Performance evaluation was performed with the sole purpose of verifying the literature reported accuracy for the selected algorithm as well as to compute its identification speed on two databases (CASIA and BuIris) containing 600 iris images each. Results obtained matched the earlier 0% false acceptance with CASIA database but 42.3% with BuIris. This paper results verifies the scope of this algorithm and the need for improvement that could increase its adoptability globally.

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Index Terms

Computer Science
Information Sciences


Iris recognition biometrics empirical analysis Casia BuIris