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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/5552/1/fskkp-2013-suryanti-feature_level.pdf
http://umpir.ump.edu.my/id/eprint/5552/
http://dx.doi.org/10.1109/SITIS.2013.115
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spelling my.ump.umpir.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
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
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/5552/1/fskkp-2013-suryanti-feature_level.pdf
http://umpir.ump.edu.my/id/eprint/5552/
http://dx.doi.org/10.1109/SITIS.2013.115
_version_ 1643665212119711744
score 13.211869

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