Feature Level Fusion of Face and Signature Using a Modified Feature Selection Technique

The multimodal biometric which is a combination of two or more modalities of biometric is able to give more assurance for the securities of some systems. Feature level fusion has been shown to provide higher-performance accuracy and provide a more secure recognition system. In this paper, we propo...

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Main Authors:	Suryanti, Awang, Rubiyah, Yusof, Mohamad Fairol, Zamzuri, Reza, Arfa
Format:	Conference or Workshop Item
Language:	English
Published:	2013
Subjects:	QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/5552/ http://umpir.ump.edu.my/id/eprint/5552/ http://umpir.ump.edu.my/id/eprint/5552/ http://umpir.ump.edu.my/id/eprint/5552/1/fskkp-2013-suryanti-feature_level.pdf

id	ump-5552
recordtype	eprints
spelling	ump-55522018-02-06T06:34:54Z http://umpir.ump.edu.my/id/eprint/5552/ Feature Level Fusion of Face and Signature Using a Modified Feature Selection Technique Suryanti, Awang Rubiyah, Yusof Mohamad Fairol, Zamzuri Reza, Arfa QA76 Computer software The multimodal biometric which is a combination of two or more modalities of biometric is able to give more assurance for the securities of some systems. Feature level fusion has been shown to provide higher-performance accuracy and provide a more secure recognition system. In this paper, we propose a feature level fusion of face features which are the physical appearance of a person in image-based and the online handwritten signature features which are the behavioral characteristics of a person in dynamic-based. The problem of high dimensionality of the combined features is overcome by the used of Linear Discriminant Analysis (LDA) in the feature extraction phase. One challenge in multi modal feature level fusion is to maintain the balance of the features selected between the two modalities, otherwise one modality may outweigh another. In order to address this issue, we propose to perform feature fusion in the feature selection phase. Feature selection using GA with modified fitness function is applied to the concatenated features in order to ensure that only significant and most balanced features are used for classification. Comparison of the performance of the proposed method with other approaches indicates the highest in the recognition accuracy of 97.50%. 2013 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/5552/1/fskkp-2013-suryanti-feature_level.pdf Suryanti, Awang and Rubiyah, Yusof and Mohamad Fairol, Zamzuri and Reza, Arfa (2013) Feature Level Fusion of Face and Signature Using a Modified Feature Selection Technique. In: International Conference on FSignal-Image Technology & Internet-Based Systems (SITIS 2013), 2-5 December 2013 , Kyoto. pp. 706-713.. http://dx.doi.org/10.1109/SITIS.2013.115 doi:10.1109/SITIS.2013.115
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Suryanti, Awang Rubiyah, Yusof Mohamad Fairol, Zamzuri Reza, Arfa Feature Level Fusion of Face and Signature Using a Modified Feature Selection Technique
description	The multimodal biometric which is a combination of two or more modalities of biometric is able to give more assurance for the securities of some systems. Feature level fusion has been shown to provide higher-performance accuracy and provide a more secure recognition system. In this paper, we propose a feature level fusion of face features which are the physical appearance of a person in image-based and the online handwritten signature features which are the behavioral characteristics of a person in dynamic-based. The problem of high dimensionality of the combined features is overcome by the used of Linear Discriminant Analysis (LDA) in the feature extraction phase. One challenge in multi modal feature level fusion is to maintain the balance of the features selected between the two modalities, otherwise one modality may outweigh another. In order to address this issue, we propose to perform feature fusion in the feature selection phase. Feature selection using GA with modified fitness function is applied to the concatenated features in order to ensure that only significant and most balanced features are used for classification. Comparison of the performance of the proposed method with other approaches indicates the highest in the recognition accuracy of 97.50%.
format	Conference or Workshop Item
author	Suryanti, Awang Rubiyah, Yusof Mohamad Fairol, Zamzuri Reza, Arfa
author_facet	Suryanti, Awang Rubiyah, Yusof Mohamad Fairol, Zamzuri Reza, Arfa
author_sort	Suryanti, Awang
title	Feature Level Fusion of Face and Signature Using a Modified Feature Selection Technique
title_short	Feature Level Fusion of Face and Signature Using a Modified Feature Selection Technique
title_full	Feature Level Fusion of Face and Signature Using a Modified Feature Selection Technique
title_fullStr	Feature Level Fusion of Face and Signature Using a Modified Feature Selection Technique
title_full_unstemmed	Feature Level Fusion of Face and Signature Using a Modified Feature Selection Technique
title_sort	feature level fusion of face and signature using a modified feature selection technique
publishDate	2013
url	http://umpir.ump.edu.my/id/eprint/5552/ http://umpir.ump.edu.my/id/eprint/5552/ http://umpir.ump.edu.my/id/eprint/5552/ http://umpir.ump.edu.my/id/eprint/5552/1/fskkp-2013-suryanti-feature_level.pdf
first_indexed	2023-09-18T22:00:53Z
last_indexed	2023-09-18T22:00:53Z
_version_	1777414390348251136

Feature Level Fusion of Face and Signature Using a Modified Feature Selection Technique

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