Secure Multimodal Biometric Fusion using Fingerprint and Finger Vein Descriptors

Biometrics recognition is a method to verify the identity of a person using a physical or behavioural characteristic. To date, biometrics is widely used as an alternative to password authentication. Fingerprint is of the oldest biometric technique. It uses the patterns of ridge and valley which can...

Full description

Saved in:

Bibliographic Details
Main Author:	Munalih, Ahmad Syarif
Format:	Thesis
Published:	2014
Subjects:	TK7800-8360 Electronics
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-mmu-ep.6880
record_format	uketd_dc
spelling	my-mmu-ep.68802017-09-06T15:41:42Z Secure Multimodal Biometric Fusion using Fingerprint and Finger Vein Descriptors 2014-11 Munalih, Ahmad Syarif TK7800-8360 Electronics Biometrics recognition is a method to verify the identity of a person using a physical or behavioural characteristic. To date, biometrics is widely used as an alternative to password authentication. Fingerprint is of the oldest biometric technique. It uses the patterns of ridge and valley which can be found on the surface of human finger to recognize human identity. Meanwhile, finger vein biometrics is one of the newest biometric technique. Finger vein biometrics uses the pattern of blood vein inside human finger to recognize human identity. Both fingerprint and finger vein biometrics are the main focus of study in this research. A fingerprint feature extraction method, namely Histogram of Oriented Gradient (HOG) is explored and implemented. Besides, an improved finger vein feature extraction method termed as enhanced maximum curvature (EMC) methods is proposed. When EMC is combined with HOG, delicate vein line pattern can be extracted effectively. The proposed method is able to produce better performance as compared to the existing finger vein feature extraction methods. Multimodal biometrics combines more than one biometric sources to address problems such as high intra-class variations, high inter-class similarity and noisy data in unimodal biometrics. In this work, a novel method to combine the HOG features of fingerprint and finger vein biometrics is designed based on Kernel Data Fusion. The method converts the fingerprint and finger vein features into matrix kernels and uses Support Vector Machine for classification. The proposed method has yielded significantly improvement as compared to sole fingerprint or finger vein biometrics as over 99% of recognition accuracy can be achieved with the use of RBF Kernel. Another focus of this thesis is to secure the biometric system to address the revocability and privacy issues. Biohashing is the solution explored in this research. 2014-11 Thesis http://shdl.mmu.edu.my/6880/ http://library.mmu.edu.my/diglib/onlinedb/dig_lib.php masters Multimedia University Faculty of Information Science and Technology
institution	Multimedia University
collection	MMU Institutional Repository
topic	TK7800-8360 Electronics
spellingShingle	TK7800-8360 Electronics Munalih, Ahmad Syarif Secure Multimodal Biometric Fusion using Fingerprint and Finger Vein Descriptors
description	Biometrics recognition is a method to verify the identity of a person using a physical or behavioural characteristic. To date, biometrics is widely used as an alternative to password authentication. Fingerprint is of the oldest biometric technique. It uses the patterns of ridge and valley which can be found on the surface of human finger to recognize human identity. Meanwhile, finger vein biometrics is one of the newest biometric technique. Finger vein biometrics uses the pattern of blood vein inside human finger to recognize human identity. Both fingerprint and finger vein biometrics are the main focus of study in this research. A fingerprint feature extraction method, namely Histogram of Oriented Gradient (HOG) is explored and implemented. Besides, an improved finger vein feature extraction method termed as enhanced maximum curvature (EMC) methods is proposed. When EMC is combined with HOG, delicate vein line pattern can be extracted effectively. The proposed method is able to produce better performance as compared to the existing finger vein feature extraction methods. Multimodal biometrics combines more than one biometric sources to address problems such as high intra-class variations, high inter-class similarity and noisy data in unimodal biometrics. In this work, a novel method to combine the HOG features of fingerprint and finger vein biometrics is designed based on Kernel Data Fusion. The method converts the fingerprint and finger vein features into matrix kernels and uses Support Vector Machine for classification. The proposed method has yielded significantly improvement as compared to sole fingerprint or finger vein biometrics as over 99% of recognition accuracy can be achieved with the use of RBF Kernel. Another focus of this thesis is to secure the biometric system to address the revocability and privacy issues. Biohashing is the solution explored in this research.
format	Thesis
qualification_level	Master's degree
author	Munalih, Ahmad Syarif
author_facet	Munalih, Ahmad Syarif
author_sort	Munalih, Ahmad Syarif
title	Secure Multimodal Biometric Fusion using Fingerprint and Finger Vein Descriptors
title_short	Secure Multimodal Biometric Fusion using Fingerprint and Finger Vein Descriptors
title_full	Secure Multimodal Biometric Fusion using Fingerprint and Finger Vein Descriptors
title_fullStr	Secure Multimodal Biometric Fusion using Fingerprint and Finger Vein Descriptors
title_full_unstemmed	Secure Multimodal Biometric Fusion using Fingerprint and Finger Vein Descriptors
title_sort	secure multimodal biometric fusion using fingerprint and finger vein descriptors
granting_institution	Multimedia University
granting_department	Faculty of Information Science and Technology
publishDate	2014
_version_	1747829639444692992

Secure Multimodal Biometric Fusion using Fingerprint and Finger Vein Descriptors

Similar Items