Evaluation of fusion score for face verification system

Given an individual face image and a claimed ID, the face verification problem is to determine whether or not he is the person he claims to be. Although this task seems to be easy for a human, this problem is one of the most challenging problems in the area of computer vision. Eigenface and fisherfa...

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Main Author:	Arfa, Reza
Format:	Thesis
Language:	English
Published:	2013
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/33158/5/RezaArfa-MFKE2013.pdf
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id	my-utm-ep.33158
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spelling	my-utm-ep.331582017-09-12T06:23:02Z Evaluation of fusion score for face verification system 2013-01 Arfa, Reza TK Electrical engineering. Electronics Nuclear engineering Given an individual face image and a claimed ID, the face verification problem is to determine whether or not he is the person he claims to be. Although this task seems to be easy for a human, this problem is one of the most challenging problems in the area of computer vision. Eigenface and fisherface are two well-known and successful face verification approaches. Despite an assumption that face verification systems based on fisherface is thought to be more accurate than eigenface system, recent studies reveal that the idea is not always true. In this research, in order to leverage on the strength of both eigenface and fisherface techniques, a fusion of these two techniques by using different fusion method is examined. Four fusion methods, namely, sum-rule, Artificial Neural Network (ANN), Linear Support Vector Machines (Linear SVM), and Gaussian Support Vector Machines (Gaussian SVM) are considered. ORL database is used to evaluate and compare different approaches. The experiments show that the Total Error Rate for individual eigenface and fisherface systems are 12.5% and 9.4% respectively. This error for the fusion based systems that use sum-rule, ANN, Linear SVM, and Gaussian SVM, as fusion techniques are 9.9%, 5.9%, 6.7%, and 6.3% respectively. The results demonstrate that fusion-based face verification system outperforms both eigenface and fisherface systems when used individually 2013-01 Thesis http://eprints.utm.my/id/eprint/33158/ http://eprints.utm.my/id/eprint/33158/5/RezaArfa-MFKE2013.pdf application/pdf en public masters 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 Arfa, Reza Evaluation of fusion score for face verification system
description	Given an individual face image and a claimed ID, the face verification problem is to determine whether or not he is the person he claims to be. Although this task seems to be easy for a human, this problem is one of the most challenging problems in the area of computer vision. Eigenface and fisherface are two well-known and successful face verification approaches. Despite an assumption that face verification systems based on fisherface is thought to be more accurate than eigenface system, recent studies reveal that the idea is not always true. In this research, in order to leverage on the strength of both eigenface and fisherface techniques, a fusion of these two techniques by using different fusion method is examined. Four fusion methods, namely, sum-rule, Artificial Neural Network (ANN), Linear Support Vector Machines (Linear SVM), and Gaussian Support Vector Machines (Gaussian SVM) are considered. ORL database is used to evaluate and compare different approaches. The experiments show that the Total Error Rate for individual eigenface and fisherface systems are 12.5% and 9.4% respectively. This error for the fusion based systems that use sum-rule, ANN, Linear SVM, and Gaussian SVM, as fusion techniques are 9.9%, 5.9%, 6.7%, and 6.3% respectively. The results demonstrate that fusion-based face verification system outperforms both eigenface and fisherface systems when used individually
format	Thesis
qualification_level	Master's degree
author	Arfa, Reza
author_facet	Arfa, Reza
author_sort	Arfa, Reza
title	Evaluation of fusion score for face verification system
title_short	Evaluation of fusion score for face verification system
title_full	Evaluation of fusion score for face verification system
title_fullStr	Evaluation of fusion score for face verification system
title_full_unstemmed	Evaluation of fusion score for face verification system
title_sort	evaluation of fusion score for face verification system
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2013
url	http://eprints.utm.my/id/eprint/33158/5/RezaArfa-MFKE2013.pdf
_version_	1747816093524688896

Evaluation of fusion score for face verification system

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