Soft biometric system using fuzzy logic decision fusion for identification

Traditional biometrics such as fingerprint, retina and iris are highly accurate and efficient biometric modalities. However, their disadvantages include intrusiveness, inability to work at far distance, and requirement of human cooperation to function effectively. On the other hand, some biometric a...

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Main Author:	Ayodeji, Arigbabu Olasimbo
Format:	Thesis
Language:	English
Published:	2014
Subjects:	Biometric identification Fuzzy logic
Online Access:	http://psasir.upm.edu.my/id/eprint/47972/1/FK%202014%2023R.pdf
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id	my-upm-ir.47972
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spelling	my-upm-ir.479722017-01-26T08:46:22Z Soft biometric system using fuzzy logic decision fusion for identification 2014-08 Ayodeji, Arigbabu Olasimbo Traditional biometrics such as fingerprint, retina and iris are highly accurate and efficient biometric modalities. However, their disadvantages include intrusiveness, inability to work at far distance, and requirement of human cooperation to function effectively. On the other hand, some biometric applications such as identification of humans from distance may not require a high degree of accuracy, yet they cannot tolerate the need for human cooperation and intrusiveness. Therefore, soft biometrics is an alternative biometric modality to perform identification of humans from distance. In this situation, soft biometrics can provide a moderate level of identification when the subjects are not cooperative with acquisition system. In addition, intrusiveness issues can be sufficiently minimized by using soft biometrics, because the attribute can be extracted without interaction with the subjects. In this thesis, the possibility of using multiple soft biometrics for identification is investigated. The thesis shows that when multiple soft biometric attributes are combined together, they can be logically applied to find a specific identity in the database. The main focus is placed on combining face and body related soft biometric attributes like facial shape, skin colour, height, and weight for identification purposes. Here, each attribute performs individual identification process, which includes sub-processes of pre-processing, feature extraction, and template matching. This is followed by decision fusion which combines the identification decisions of all soft biometrics to find a particular target in the list of subjects with the closest resemblance in the database. Two main contributions are presented in this thesis. First, techniques for extracting facial shape, height, and body weight are proposed. Second, the thesis evaluates three match score fusion techniques such as SUM, Adaptive Weighted SUM, and Fuzzy Logic to determine the most reliable fusion technique for the soft biometric identification system. The results demonstrate that soft biometric identification system that utilizes fuzzy decision fusion which is based on facial shape, height, and weight is the most optimum with a rank-1 identification rate of 88%. Biometric identification Fuzzy logic 2014-08 Thesis http://psasir.upm.edu.my/id/eprint/47972/ http://psasir.upm.edu.my/id/eprint/47972/1/FK%202014%2023R.pdf application/pdf en public masters Universiti Putra Malaysia Biometric identification Fuzzy logic
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Biometric identification Fuzzy logic
spellingShingle	Biometric identification Fuzzy logic Ayodeji, Arigbabu Olasimbo Soft biometric system using fuzzy logic decision fusion for identification
description	Traditional biometrics such as fingerprint, retina and iris are highly accurate and efficient biometric modalities. However, their disadvantages include intrusiveness, inability to work at far distance, and requirement of human cooperation to function effectively. On the other hand, some biometric applications such as identification of humans from distance may not require a high degree of accuracy, yet they cannot tolerate the need for human cooperation and intrusiveness. Therefore, soft biometrics is an alternative biometric modality to perform identification of humans from distance. In this situation, soft biometrics can provide a moderate level of identification when the subjects are not cooperative with acquisition system. In addition, intrusiveness issues can be sufficiently minimized by using soft biometrics, because the attribute can be extracted without interaction with the subjects. In this thesis, the possibility of using multiple soft biometrics for identification is investigated. The thesis shows that when multiple soft biometric attributes are combined together, they can be logically applied to find a specific identity in the database. The main focus is placed on combining face and body related soft biometric attributes like facial shape, skin colour, height, and weight for identification purposes. Here, each attribute performs individual identification process, which includes sub-processes of pre-processing, feature extraction, and template matching. This is followed by decision fusion which combines the identification decisions of all soft biometrics to find a particular target in the list of subjects with the closest resemblance in the database. Two main contributions are presented in this thesis. First, techniques for extracting facial shape, height, and body weight are proposed. Second, the thesis evaluates three match score fusion techniques such as SUM, Adaptive Weighted SUM, and Fuzzy Logic to determine the most reliable fusion technique for the soft biometric identification system. The results demonstrate that soft biometric identification system that utilizes fuzzy decision fusion which is based on facial shape, height, and weight is the most optimum with a rank-1 identification rate of 88%.
format	Thesis
qualification_level	Master's degree
author	Ayodeji, Arigbabu Olasimbo
author_facet	Ayodeji, Arigbabu Olasimbo
author_sort	Ayodeji, Arigbabu Olasimbo
title	Soft biometric system using fuzzy logic decision fusion for identification
title_short	Soft biometric system using fuzzy logic decision fusion for identification
title_full	Soft biometric system using fuzzy logic decision fusion for identification
title_fullStr	Soft biometric system using fuzzy logic decision fusion for identification
title_full_unstemmed	Soft biometric system using fuzzy logic decision fusion for identification
title_sort	soft biometric system using fuzzy logic decision fusion for identification
granting_institution	Universiti Putra Malaysia
publishDate	2014
url	http://psasir.upm.edu.my/id/eprint/47972/1/FK%202014%2023R.pdf
_version_	1747811958534438912

Soft biometric system using fuzzy logic decision fusion for identification

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