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 propose...
| Главные авторы: | , , , |
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| Формат: | Conference or Workshop Item |
| Язык: | English |
| Опубликовано: |
2013
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| Предметы: | |
| Online-ссылка: | http://umpir.ump.edu.my/id/eprint/5552/1/fskkp-2013-suryanti-feature_level.pdf |
| _version_ | 1825821662491181056 |
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| 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 |
| collection | UMP |
| 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%. |
| first_indexed | 2024-03-06T11:44:56Z |
| format | Conference or Workshop Item |
| id | UMPir5552 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T11:44:56Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | UMPir55522024-01-08T01:33:02Z 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.. (Published) http://dx.doi.org/10.1109/SITIS.2013.115 doi:10.1109/SITIS.2013.115 |
| 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 |
| title | 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_short | 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 |
| topic | QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/5552/1/fskkp-2013-suryanti-feature_level.pdf |
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