Model of Bayesian tangent eye shape for eye capture

Iris recognition system captures an image of an individual's eye. In addition, the process of segmentation, normalization and feature extraction is followed by the iris of an eye image in the system. Using the algorithms proposed by J. Daugman, Iris recognition system has significantly improved...

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Main Authors: Nsaef, Asama Kuder, Jaafar, Azizah, Sliman, Layth, Sulaiman, Riza, O. K. Rahmat, Rahmita Wirza
Format: Conference or Workshop Item
Language:English
Published: IEEE 2014
Online Access:http://psasir.upm.edu.my/id/eprint/69610/1/Model%20of%20Bayesian%20tangent%20eye%20shape%20for%20eye%20capture.pdf
http://psasir.upm.edu.my/id/eprint/69610/
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spelling my.upm.eprints.696102019-07-08T02:02:57Z http://psasir.upm.edu.my/id/eprint/69610/ Model of Bayesian tangent eye shape for eye capture Nsaef, Asama Kuder Jaafar, Azizah Sliman, Layth Sulaiman, Riza O. K. Rahmat, Rahmita Wirza Iris recognition system captures an image of an individual's eye. In addition, the process of segmentation, normalization and feature extraction is followed by the iris of an eye image in the system. Using the algorithms proposed by J. Daugman, Iris recognition system has significantly improved over the last decade, and it has been used in so many practical applications. However, some difficulties related to Iris position and movement are still to be improved. To overcome these difficulties one can enhance the image acquisition process. Obtaining a method in extracting quality of eye images automatically from the video stream is the main area of interest in this study. Besides, a Bayesian inference solution called Bayesian Tangent Eye Shape Model (BTESM) was suggested depending on estimation of tangent shape. During image acquisition, constraints on the position and motion of the subjects can be decreased owing to this approach. Owing to maximum a posteriori estimation, we can identify similarity transform coefficients as well as the eye shape parameters in BTESM. To apply the maximum a posteriori procedure, tangent Eye shape vector was considered the state of the model which is hidden and expectation maximization depending on searching algorithm was adopted. Hence, after being tested and matched to future studies, the acquisitioned eye image has been proved to be adequate for Iris recognition system. IEEE 2014 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/69610/1/Model%20of%20Bayesian%20tangent%20eye%20shape%20for%20eye%20capture.pdf Nsaef, Asama Kuder and Jaafar, Azizah and Sliman, Layth and Sulaiman, Riza and O. K. Rahmat, Rahmita Wirza (2014) Model of Bayesian tangent eye shape for eye capture. In: 14th International Conference on Intelligent Systems Design and Applications (ISDA 2014), 28-30 Nov. 2014, Okinawa, Japan. (pp. 82-88). 10.1109/ISDA.2014.7066277
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Iris recognition system captures an image of an individual's eye. In addition, the process of segmentation, normalization and feature extraction is followed by the iris of an eye image in the system. Using the algorithms proposed by J. Daugman, Iris recognition system has significantly improved over the last decade, and it has been used in so many practical applications. However, some difficulties related to Iris position and movement are still to be improved. To overcome these difficulties one can enhance the image acquisition process. Obtaining a method in extracting quality of eye images automatically from the video stream is the main area of interest in this study. Besides, a Bayesian inference solution called Bayesian Tangent Eye Shape Model (BTESM) was suggested depending on estimation of tangent shape. During image acquisition, constraints on the position and motion of the subjects can be decreased owing to this approach. Owing to maximum a posteriori estimation, we can identify similarity transform coefficients as well as the eye shape parameters in BTESM. To apply the maximum a posteriori procedure, tangent Eye shape vector was considered the state of the model which is hidden and expectation maximization depending on searching algorithm was adopted. Hence, after being tested and matched to future studies, the acquisitioned eye image has been proved to be adequate for Iris recognition system.
format Conference or Workshop Item
author Nsaef, Asama Kuder
Jaafar, Azizah
Sliman, Layth
Sulaiman, Riza
O. K. Rahmat, Rahmita Wirza
spellingShingle Nsaef, Asama Kuder
Jaafar, Azizah
Sliman, Layth
Sulaiman, Riza
O. K. Rahmat, Rahmita Wirza
Model of Bayesian tangent eye shape for eye capture
author_facet Nsaef, Asama Kuder
Jaafar, Azizah
Sliman, Layth
Sulaiman, Riza
O. K. Rahmat, Rahmita Wirza
author_sort Nsaef, Asama Kuder
title Model of Bayesian tangent eye shape for eye capture
title_short Model of Bayesian tangent eye shape for eye capture
title_full Model of Bayesian tangent eye shape for eye capture
title_fullStr Model of Bayesian tangent eye shape for eye capture
title_full_unstemmed Model of Bayesian tangent eye shape for eye capture
title_sort model of bayesian tangent eye shape for eye capture
publisher IEEE
publishDate 2014
url http://psasir.upm.edu.my/id/eprint/69610/1/Model%20of%20Bayesian%20tangent%20eye%20shape%20for%20eye%20capture.pdf
http://psasir.upm.edu.my/id/eprint/69610/
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score 13.211869