Furrow and crypt detection using Modified Ant Colony Optimization for iris recognition / Zaheera Zainal Abidin

Furrow and crypt detection using Modified Ant Colony Optimization for iris recognition / Zaheera Zainal Abidin

Iris recognition has been widely recognized as one of the most performing biometric system. The accuracy performance of iris recognition system is measured by FAR (False Accept Rate) and FRR (False Reject Rate). FRR measures the genuine that is incorrectly denied by the system due to the changes in...

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Main Author: Zainal Abidin, Zaheera
Format: Book Section
Language:English
Published: Institute of Graduate Studies, UiTM 2016
Subjects:
Malaysia
Online Access:http://ir.uitm.edu.my/id/eprint/19387/
http://ir.uitm.edu.my/id/eprint/19387/1/ABS_ZAHEERA%20ZAINAL%20ABIDIN%20TDRA%20VOL%209%20IGS%2016.pdf
id uitm-19387
recordtype eprints
spelling uitm-193872018-06-11T06:09:42Z http://ir.uitm.edu.my/id/eprint/19387/ Furrow and crypt detection using Modified Ant Colony Optimization for iris recognition / Zaheera Zainal Abidin Zainal Abidin, Zaheera Malaysia Iris recognition has been widely recognized as one of the most performing biometric system. The accuracy performance of iris recognition system is measured by FAR (False Accept Rate) and FRR (False Reject Rate). FRR measures the genuine that is incorrectly denied by the system due to the changes in iris features (such as aging and health condition) and external factors that affected the iris image to be high in noise rate. The external factors such as technical fault, occlusion, and source of lighting caused the image acquisition which produce distorted iris images problem hence incorrectly rejected by the system. The current way of reducing FRR are wavelets and Gabor filters, cascaded classifiers, ordinal measure, multiple biometric modality and selection of unique iris features. Iris structure consists of unique features such as crypts, furrows, collarette, pigment blotches, freckles and pupil that are distinguishable among human. Previous research has been done in selecting the unique iris features however it shows low accuracy performance. As a solution, to improve the accuracy performance, this research proposes a new approach called as Modified Ant Colony Optimization that uses ant colony algorithm which search for crypts and radial furrow. The method consists of two tasks in obtaining the crypt and radial furrow features from the iris texture. The first task is the artificial ants that scan the pixel values according to the range of selected crypt or radial furrow. Then, the scanned pixels value is searched based on degree of angle (0o, 45o, 90o and 135o). The second task produces the confusion matrix and the blob of iris feature image is marked and indexed before stored into the database… Institute of Graduate Studies, UiTM 2016 Book Section PeerReviewed text en http://ir.uitm.edu.my/id/eprint/19387/1/ABS_ZAHEERA%20ZAINAL%20ABIDIN%20TDRA%20VOL%209%20IGS%2016.pdf Zainal Abidin, Zaheera (2016) Furrow and crypt detection using Modified Ant Colony Optimization for iris recognition / Zaheera Zainal Abidin. In: The Doctoral Research Abstracts. IGS Biannual Publication, 9 (9). Institute of Graduate Studies, UiTM, Shah Alam.
repository_type Digital Repository
institution_category Local University
institution Universiti Teknologi MARA
building UiTM Institutional Repository
collection Online Access
language English
topic Malaysia
spellingShingle Malaysia
Zainal Abidin, Zaheera
Furrow and crypt detection using Modified Ant Colony Optimization for iris recognition / Zaheera Zainal Abidin
description Iris recognition has been widely recognized as one of the most performing biometric system. The accuracy performance of iris recognition system is measured by FAR (False Accept Rate) and FRR (False Reject Rate). FRR measures the genuine that is incorrectly denied by the system due to the changes in iris features (such as aging and health condition) and external factors that affected the iris image to be high in noise rate. The external factors such as technical fault, occlusion, and source of lighting caused the image acquisition which produce distorted iris images problem hence incorrectly rejected by the system. The current way of reducing FRR are wavelets and Gabor filters, cascaded classifiers, ordinal measure, multiple biometric modality and selection of unique iris features. Iris structure consists of unique features such as crypts, furrows, collarette, pigment blotches, freckles and pupil that are distinguishable among human. Previous research has been done in selecting the unique iris features however it shows low accuracy performance. As a solution, to improve the accuracy performance, this research proposes a new approach called as Modified Ant Colony Optimization that uses ant colony algorithm which search for crypts and radial furrow. The method consists of two tasks in obtaining the crypt and radial furrow features from the iris texture. The first task is the artificial ants that scan the pixel values according to the range of selected crypt or radial furrow. Then, the scanned pixels value is searched based on degree of angle (0o, 45o, 90o and 135o). The second task produces the confusion matrix and the blob of iris feature image is marked and indexed before stored into the database…
format Book Section
author Zainal Abidin, Zaheera
author_facet Zainal Abidin, Zaheera
author_sort Zainal Abidin, Zaheera
title Furrow and crypt detection using Modified Ant Colony Optimization for iris recognition / Zaheera Zainal Abidin
title_short Furrow and crypt detection using Modified Ant Colony Optimization for iris recognition / Zaheera Zainal Abidin
title_full Furrow and crypt detection using Modified Ant Colony Optimization for iris recognition / Zaheera Zainal Abidin
title_fullStr Furrow and crypt detection using Modified Ant Colony Optimization for iris recognition / Zaheera Zainal Abidin
title_full_unstemmed Furrow and crypt detection using Modified Ant Colony Optimization for iris recognition / Zaheera Zainal Abidin
title_sort furrow and crypt detection using modified ant colony optimization for iris recognition / zaheera zainal abidin
publisher Institute of Graduate Studies, UiTM
publishDate 2016
url http://ir.uitm.edu.my/id/eprint/19387/
http://ir.uitm.edu.my/id/eprint/19387/1/ABS_ZAHEERA%20ZAINAL%20ABIDIN%20TDRA%20VOL%209%20IGS%2016.pdf
first_indexed 2023-09-18T23:02:27Z
last_indexed 2023-09-18T23:02:27Z
_version_ 1777418263880269824

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