Feature extraction using two dimensional (2D) legendre wavelet filter for partial iris recognition

An increasing need for biometrics recognition systems has grown substantially to address the issues of recognition and identification, especially in highly dense areas such as airports, train stations, and financial transactions. Evidence of these can be seen in some airports and also the impl...

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Main Author:	Muktar, Danlami
Format:	Thesis
Language:	English English English
Published:	2020
Subjects:	TA1501-1820 Applied optics Photonics
Online Access:	http://eprints.uthm.edu.my/887/1/24p%20DANLAMI%20MUKTAR.pdf http://eprints.uthm.edu.my/887/2/DANLAMI%20MUKTAR%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/887/3/DANLAMI%20MUKTAR%20WATERMARK.pdf
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spelling	my-uthm-ep.8872021-09-06T08:19:00Z Feature extraction using two dimensional (2D) legendre wavelet filter for partial iris recognition 2020-07 Muktar, Danlami TA1501-1820 Applied optics. Photonics An increasing need for biometrics recognition systems has grown substantially to address the issues of recognition and identification, especially in highly dense areas such as airports, train stations, and financial transactions. Evidence of these can be seen in some airports and also the implementation of these technologies in our mobile phones. Among the most popular biometric technologies include facial, fingerprints, and iris recognition. The iris recognition is considered by many researchers to be the most accurate and reliable form of biometric recognition because iris can neither be surgically operated with a chance of losing slight nor change due to aging. However, presently most iris recognition systems available can only recognize iris image with frontal-looking and high-quality images. Angular image and partially capture image cannot be authenticated with the existing method of iris recognition. This research investigates the possibility of developing a technique for recognition partially captured iris image. The technique is designed to process the iris image at 50%, 25%, 16.5%, and 12.5% and to find a threshold for a minimum amount of iris region required to authenticate the individual. The research also developed and implemented two Dimensional (2D) Legendre wavelet filter for the iris feature extraction. The Legendre wavelet filter is to enhance the feature extraction technique. Selected iris images from CASIA, UBIRIS, and MMU database were used to test the accuracy of the introduced technique. The technique was able to produce recognition accuracy between 70 – 90% CASIA-interval with 92.25% accuracy, CASIA-distance with 86.25%, UBIRIS with 74.95%, and MMU with 94.45%. 2020-07 Thesis http://eprints.uthm.edu.my/887/ http://eprints.uthm.edu.my/887/1/24p%20DANLAMI%20MUKTAR.pdf text en public http://eprints.uthm.edu.my/887/2/DANLAMI%20MUKTAR%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/887/3/DANLAMI%20MUKTAR%20WATERMARK.pdf text en validuser phd doctoral Universiti Tun Hussein Onn Malaysia Fakulti Sains Komputer dan Teknologi Maklumat
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	TA1501-1820 Applied optics Photonics
spellingShingle	TA1501-1820 Applied optics Photonics Muktar, Danlami Feature extraction using two dimensional (2D) legendre wavelet filter for partial iris recognition
description	An increasing need for biometrics recognition systems has grown substantially to address the issues of recognition and identification, especially in highly dense areas such as airports, train stations, and financial transactions. Evidence of these can be seen in some airports and also the implementation of these technologies in our mobile phones. Among the most popular biometric technologies include facial, fingerprints, and iris recognition. The iris recognition is considered by many researchers to be the most accurate and reliable form of biometric recognition because iris can neither be surgically operated with a chance of losing slight nor change due to aging. However, presently most iris recognition systems available can only recognize iris image with frontal-looking and high-quality images. Angular image and partially capture image cannot be authenticated with the existing method of iris recognition. This research investigates the possibility of developing a technique for recognition partially captured iris image. The technique is designed to process the iris image at 50%, 25%, 16.5%, and 12.5% and to find a threshold for a minimum amount of iris region required to authenticate the individual. The research also developed and implemented two Dimensional (2D) Legendre wavelet filter for the iris feature extraction. The Legendre wavelet filter is to enhance the feature extraction technique. Selected iris images from CASIA, UBIRIS, and MMU database were used to test the accuracy of the introduced technique. The technique was able to produce recognition accuracy between 70 – 90% CASIA-interval with 92.25% accuracy, CASIA-distance with 86.25%, UBIRIS with 74.95%, and MMU with 94.45%.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Muktar, Danlami
author_facet	Muktar, Danlami
author_sort	Muktar, Danlami
title	Feature extraction using two dimensional (2D) legendre wavelet filter for partial iris recognition
title_short	Feature extraction using two dimensional (2D) legendre wavelet filter for partial iris recognition
title_full	Feature extraction using two dimensional (2D) legendre wavelet filter for partial iris recognition
title_fullStr	Feature extraction using two dimensional (2D) legendre wavelet filter for partial iris recognition
title_full_unstemmed	Feature extraction using two dimensional (2D) legendre wavelet filter for partial iris recognition
title_sort	feature extraction using two dimensional (2d) legendre wavelet filter for partial iris recognition
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Sains Komputer dan Teknologi Maklumat
publishDate	2020
url	http://eprints.uthm.edu.my/887/1/24p%20DANLAMI%20MUKTAR.pdf http://eprints.uthm.edu.my/887/2/DANLAMI%20MUKTAR%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/887/3/DANLAMI%20MUKTAR%20WATERMARK.pdf
_version_	1747830701283082240

Feature extraction using two dimensional (2D) legendre wavelet filter for partial iris recognition

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