Feature fusion using a modified genetic algorithm for face and signature recognition system

Combination of multi biometrics at feature level fusion is able to give more accurate classification result. This thesis focuses on the development of feature level fusion of bimodal biometrics system for face and dynamic signature recognition system The modalities of biometric are used due to the a...

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Main Author:	Suryanti, Awang
Format:	Thesis
Language:	English
Published:	2015
Subjects:	QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/16629/16/Feature%20fusion%20using%20a%20modified%20genetic%20algorithm%20for%20face%20and%20signature%20recognition%20system.pdf
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spelling	my-ump-ir.166292024-01-08T01:34:54Z Feature fusion using a modified genetic algorithm for face and signature recognition system 2015-06 Suryanti, Awang QA76 Computer software Combination of multi biometrics at feature level fusion is able to give more accurate classification result. This thesis focuses on the development of feature level fusion of bimodal biometrics system for face and dynamic signature recognition system The modalities of biometric are used due to the ability to avoid spoof attack since it is difficult for impostor to imitate two different characteristics (behaviour and physical) at the same time. Most existing systems are dealing with feature fusion of the same domain such as image based of fingerprint and face. Thus, there is no issue of incompatible features to be fused compared to the proposed development. Balance of the combined features has not been assessed whereas it is essential to ensure one of the biometrics does not dominate accuracy performance. To overcome the issue of incompatible features to be combined, Wrapper Genetic Algorithm (GA) was implemented as the feature selection algorithm due to its ability to evaluate the features irrespective of which domain by masking the features with bit number. A modified fitness function in Wrapper GA was introduced by adding a function to maintain the balanced of the selected features. Penalty's value was imposed to the function when there is imbalance occurs in the selected features. Therefore, the accuracy performance of this system based on the fitness function that will rely on the percentage of correctly recognized samples and the balanced of selected features. Several approaches and benchmark data were used to validate the effectiveness of the proposed method compared to the unimodal system and normal feature selection method. Results show that the proposed method yield optimal recognition with the highest accuracy of 97.50%. In addition, the importance of both biometrics remains, while maintaining the balance of the selected features. 2015-06 Thesis http://umpir.ump.edu.my/id/eprint/16629/ http://umpir.ump.edu.my/id/eprint/16629/16/Feature%20fusion%20using%20a%20modified%20genetic%20algorithm%20for%20face%20and%20signature%20recognition%20system.pdf pdf en public phd doctoral Universiti Teknologi Malaysia Faculty of Electrical Engineering
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
collection	UMPSA Institutional Repository
language	English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Suryanti, Awang Feature fusion using a modified genetic algorithm for face and signature recognition system
description	Combination of multi biometrics at feature level fusion is able to give more accurate classification result. This thesis focuses on the development of feature level fusion of bimodal biometrics system for face and dynamic signature recognition system The modalities of biometric are used due to the ability to avoid spoof attack since it is difficult for impostor to imitate two different characteristics (behaviour and physical) at the same time. Most existing systems are dealing with feature fusion of the same domain such as image based of fingerprint and face. Thus, there is no issue of incompatible features to be fused compared to the proposed development. Balance of the combined features has not been assessed whereas it is essential to ensure one of the biometrics does not dominate accuracy performance. To overcome the issue of incompatible features to be combined, Wrapper Genetic Algorithm (GA) was implemented as the feature selection algorithm due to its ability to evaluate the features irrespective of which domain by masking the features with bit number. A modified fitness function in Wrapper GA was introduced by adding a function to maintain the balanced of the selected features. Penalty's value was imposed to the function when there is imbalance occurs in the selected features. Therefore, the accuracy performance of this system based on the fitness function that will rely on the percentage of correctly recognized samples and the balanced of selected features. Several approaches and benchmark data were used to validate the effectiveness of the proposed method compared to the unimodal system and normal feature selection method. Results show that the proposed method yield optimal recognition with the highest accuracy of 97.50%. In addition, the importance of both biometrics remains, while maintaining the balance of the selected features.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Suryanti, Awang
author_facet	Suryanti, Awang
author_sort	Suryanti, Awang
title	Feature fusion using a modified genetic algorithm for face and signature recognition system
title_short	Feature fusion using a modified genetic algorithm for face and signature recognition system
title_full	Feature fusion using a modified genetic algorithm for face and signature recognition system
title_fullStr	Feature fusion using a modified genetic algorithm for face and signature recognition system
title_full_unstemmed	Feature fusion using a modified genetic algorithm for face and signature recognition system
title_sort	feature fusion using a modified genetic algorithm for face and signature recognition system
granting_institution	Universiti Teknologi Malaysia
granting_department	Faculty of Electrical Engineering
publishDate	2015
url	http://umpir.ump.edu.my/id/eprint/16629/16/Feature%20fusion%20using%20a%20modified%20genetic%20algorithm%20for%20face%20and%20signature%20recognition%20system.pdf
_version_	1794020290583330816

Feature fusion using a modified genetic algorithm for face and signature recognition system

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