Adaptive noise reduction and code matching for IRIS pattern recognition system

Among all biometric modalities, iris is becoming more popular due to its high performance in recognizing or verifying individuals. Iris recognition has been used in numerous fields such as authentications at prisons, airports, banks and healthcare. Although iris recognition system has high accuracy...

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Main Author:	Dehkordi, Arezou Banitalebi
Format:	Thesis
Language:	English
Published:	2016
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/77837/1/ArezaoBanitalebiDehkordiPFKE2016.pdf
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id	my-utm-ep.77837
record_format	uketd_dc
spelling	my-utm-ep.778372018-07-04T11:49:32Z Adaptive noise reduction and code matching for IRIS pattern recognition system 2016-01 Dehkordi, Arezou Banitalebi TK Electrical engineering. Electronics Nuclear engineering Among all biometric modalities, iris is becoming more popular due to its high performance in recognizing or verifying individuals. Iris recognition has been used in numerous fields such as authentications at prisons, airports, banks and healthcare. Although iris recognition system has high accuracy with very low false acceptance rate, the system performance can still be affected by noise. Very low intensity value of eyelash pixels or high intensity values of eyelids and light reflection pixels cause inappropriate threshold values, and therefore, degrade the accuracy of system. To reduce the effects of noise and improve the accuracy of an iris recognition system, a robust algorithm consisting of two main components is proposed. First, an Adaptive Fuzzy Switching Noise Reduction (AFSNR) filter is proposed. This filter is able to reduce the effects of noise with different densities by employing fuzzy switching between adaptive median filter and filling method. Next, an Adaptive Weighted Shifting Hamming Distance (AWSHD) is proposed which improves the performance of iris code matching stage and level of decidability of the system. As a result, the proposed AFSNR filter with its adaptive window size successfully reduces the effects ofdifferent types of noise with different densities. By applying the proposed AWSHD, the distance corresponding to a genuine user is reduced, while the distance for impostors is increased. Consequently, the genuine user is more likely to be authenticated and the impostor is more likely to be rejected. Experimental results show that the proposed algorithm with genuine acceptance rate (GAR) of 99.98% and is accurate to enhance the performance of the iris recognition system. 2016-01 Thesis http://eprints.utm.my/id/eprint/77837/ http://eprints.utm.my/id/eprint/77837/1/ArezaoBanitalebiDehkordiPFKE2016.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:97607 phd doctoral Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Dehkordi, Arezou Banitalebi Adaptive noise reduction and code matching for IRIS pattern recognition system
description	Among all biometric modalities, iris is becoming more popular due to its high performance in recognizing or verifying individuals. Iris recognition has been used in numerous fields such as authentications at prisons, airports, banks and healthcare. Although iris recognition system has high accuracy with very low false acceptance rate, the system performance can still be affected by noise. Very low intensity value of eyelash pixels or high intensity values of eyelids and light reflection pixels cause inappropriate threshold values, and therefore, degrade the accuracy of system. To reduce the effects of noise and improve the accuracy of an iris recognition system, a robust algorithm consisting of two main components is proposed. First, an Adaptive Fuzzy Switching Noise Reduction (AFSNR) filter is proposed. This filter is able to reduce the effects of noise with different densities by employing fuzzy switching between adaptive median filter and filling method. Next, an Adaptive Weighted Shifting Hamming Distance (AWSHD) is proposed which improves the performance of iris code matching stage and level of decidability of the system. As a result, the proposed AFSNR filter with its adaptive window size successfully reduces the effects ofdifferent types of noise with different densities. By applying the proposed AWSHD, the distance corresponding to a genuine user is reduced, while the distance for impostors is increased. Consequently, the genuine user is more likely to be authenticated and the impostor is more likely to be rejected. Experimental results show that the proposed algorithm with genuine acceptance rate (GAR) of 99.98% and is accurate to enhance the performance of the iris recognition system.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Dehkordi, Arezou Banitalebi
author_facet	Dehkordi, Arezou Banitalebi
author_sort	Dehkordi, Arezou Banitalebi
title	Adaptive noise reduction and code matching for IRIS pattern recognition system
title_short	Adaptive noise reduction and code matching for IRIS pattern recognition system
title_full	Adaptive noise reduction and code matching for IRIS pattern recognition system
title_fullStr	Adaptive noise reduction and code matching for IRIS pattern recognition system
title_full_unstemmed	Adaptive noise reduction and code matching for IRIS pattern recognition system
title_sort	adaptive noise reduction and code matching for iris pattern recognition system
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2016
url	http://eprints.utm.my/id/eprint/77837/1/ArezaoBanitalebiDehkordiPFKE2016.pdf
_version_	1747817843161825280

Adaptive noise reduction and code matching for IRIS pattern recognition system

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