Localizing non-ideal irises via Chan-Vese model and variation level set of active contours without re-initializing

Biometrics is the science of recognizing the identity of a person based on the physical or behavioral characteristics of the individual such as signature, face, fingerprint, voice and iris. With a growing emphasis on human identification, iris recognition has recently received increasing attention....

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Main Author:	Mohammed Ali, Qadir Kamal
Format:	Thesis
Language:	English
Published:	2013
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/36845/5/QadirKamalMohammedAliMFSKSM2013.pdf
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id	my-utm-ep.36845
record_format	uketd_dc
spelling	my-utm-ep.368452017-07-06T03:31:23Z Localizing non-ideal irises via Chan-Vese model and variation level set of active contours without re-initializing 2013-07 Mohammed Ali, Qadir Kamal QA75 Electronic computers. Computer science Biometrics is the science of recognizing the identity of a person based on the physical or behavioral characteristics of the individual such as signature, face, fingerprint, voice and iris. With a growing emphasis on human identification, iris recognition has recently received increasing attention. Performance of iris recognition scheme depends on the isolation of the iris region from rest of the eye image. In this research, Iris as one of the components of an eye image is chosen due to its uniqueness and stability. Iris recognition scheme involves Acquisition, Localization, Normalization, Feature extraction and Matching. Iris localization is the most significant and crucial stage in iris recognition system, because it determines the inner boundary and outer boundary in an eye image. In conventional localization methods, the inner and outer boundaries are modeled as two circles, but in actual fact, both boundaries are near-circular contour rather than perfect circles. For this research, the non-ideal iris images which are acquired in unconstrained environments are used (i.e. image with bright spots, non uniform intensity, eyelids and eyelashes occlusion). Firstly, Gaussian filter is applied as pre-processing to reduce the iris image noises and then Chan-Vese model to detect the inner boundary and localize pupil region. Next, Gaussian filter is applied again to reduce the effect of eyelids and eyelashes for faster and easier detection of the outer boundary. Finally, Variational Level Set Formulation of Active Contours without Re-initialization is applied to localize the outer boundary. Experimental results of CASIA-Iris-Interval Version 3 database show that the performance of the proposed method is very encouraging with 98.39% accuracy rate. 2013-07 Thesis http://eprints.utm.my/id/eprint/36845/ http://eprints.utm.my/id/eprint/36845/5/QadirKamalMohammedAliMFSKSM2013.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Mohammed Ali, Qadir Kamal Localizing non-ideal irises via Chan-Vese model and variation level set of active contours without re-initializing
description	Biometrics is the science of recognizing the identity of a person based on the physical or behavioral characteristics of the individual such as signature, face, fingerprint, voice and iris. With a growing emphasis on human identification, iris recognition has recently received increasing attention. Performance of iris recognition scheme depends on the isolation of the iris region from rest of the eye image. In this research, Iris as one of the components of an eye image is chosen due to its uniqueness and stability. Iris recognition scheme involves Acquisition, Localization, Normalization, Feature extraction and Matching. Iris localization is the most significant and crucial stage in iris recognition system, because it determines the inner boundary and outer boundary in an eye image. In conventional localization methods, the inner and outer boundaries are modeled as two circles, but in actual fact, both boundaries are near-circular contour rather than perfect circles. For this research, the non-ideal iris images which are acquired in unconstrained environments are used (i.e. image with bright spots, non uniform intensity, eyelids and eyelashes occlusion). Firstly, Gaussian filter is applied as pre-processing to reduce the iris image noises and then Chan-Vese model to detect the inner boundary and localize pupil region. Next, Gaussian filter is applied again to reduce the effect of eyelids and eyelashes for faster and easier detection of the outer boundary. Finally, Variational Level Set Formulation of Active Contours without Re-initialization is applied to localize the outer boundary. Experimental results of CASIA-Iris-Interval Version 3 database show that the performance of the proposed method is very encouraging with 98.39% accuracy rate.
format	Thesis
qualification_level	Master's degree
author	Mohammed Ali, Qadir Kamal
author_facet	Mohammed Ali, Qadir Kamal
author_sort	Mohammed Ali, Qadir Kamal
title	Localizing non-ideal irises via Chan-Vese model and variation level set of active contours without re-initializing
title_short	Localizing non-ideal irises via Chan-Vese model and variation level set of active contours without re-initializing
title_full	Localizing non-ideal irises via Chan-Vese model and variation level set of active contours without re-initializing
title_fullStr	Localizing non-ideal irises via Chan-Vese model and variation level set of active contours without re-initializing
title_full_unstemmed	Localizing non-ideal irises via Chan-Vese model and variation level set of active contours without re-initializing
title_sort	localizing non-ideal irises via chan-vese model and variation level set of active contours without re-initializing
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2013
url	http://eprints.utm.my/id/eprint/36845/5/QadirKamalMohammedAliMFSKSM2013.pdf
_version_	1747816467063111680

Localizing non-ideal irises via Chan-Vese model and variation level set of active contours without re-initializing

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