Fusion of face and signature at the feature level by using correlation pattern recognition
A combination of more than one biometric is the enhancement of unimodal biometric. It is called multimodal biometric. A feature level fusion is one the fusion level. To date, feature level fusion is less implemented due to the difficulties in combining the feature from different modalities. We combi...
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my.utm.458852017-07-06T03:54:03Z http://eprints.utm.my/id/eprint/45885/ Fusion of face and signature at the feature level by using correlation pattern recognition Yusof, Rubiyah Awang, Suryanti T Technology (General) A combination of more than one biometric is the enhancement of unimodal biometric. It is called multimodal biometric. A feature level fusion is one the fusion level. To date, feature level fusion is less implemented due to the difficulties in combining the feature from different modalities. We combined the feature of face and signature that from the different domain. Correlation pattern recognition with MACE filter is used to overcome the problem of different domain. By using MACE filter, we are able to extract the feature from face and signature and produce a new fused feature vector in a frequency domain. We used a threshold specification to identify the sample testing that genuine or impostor. The Genuine Acceptance Rate (GAR) and False Acceptance Rate (FAR) are the component to evaluate the system performance. The proposed work is able to achieve preliminary GAR of 85.71 % and FAR of 14.29%-20%. Keywords—multimodal biometrics, feature level fusion, correlation pattern recognition. B I. 2011 Conference or Workshop Item PeerReviewed Yusof, Rubiyah and Awang, Suryanti (2011) Fusion of face and signature at the feature level by using correlation pattern recognition. In: International Conference On Electrical And Computer Engineering (ICECE 2011). http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.294.5629 |
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T Technology (General) Yusof, Rubiyah Awang, Suryanti Fusion of face and signature at the feature level by using correlation pattern recognition |
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A combination of more than one biometric is the enhancement of unimodal biometric. It is called multimodal biometric. A feature level fusion is one the fusion level. To date, feature level fusion is less implemented due to the difficulties in combining the feature from different modalities. We combined the feature of face and signature that from the different domain. Correlation pattern recognition with MACE filter is used to overcome the problem of different domain. By using MACE filter, we are able to extract the feature from face and signature and produce a new fused feature vector in a frequency domain. We used a threshold specification to identify the sample testing that genuine or impostor. The Genuine Acceptance Rate (GAR) and False Acceptance Rate (FAR) are the component to evaluate the system performance. The proposed work is able to achieve preliminary GAR of 85.71 % and FAR of 14.29%-20%. Keywords—multimodal biometrics, feature level fusion, correlation pattern recognition. B I. |
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Conference or Workshop Item |
author |
Yusof, Rubiyah Awang, Suryanti |
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Yusof, Rubiyah Awang, Suryanti |
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Yusof, Rubiyah |
title |
Fusion of face and signature at the feature level by using correlation pattern recognition |
title_short |
Fusion of face and signature at the feature level by using correlation pattern recognition |
title_full |
Fusion of face and signature at the feature level by using correlation pattern recognition |
title_fullStr |
Fusion of face and signature at the feature level by using correlation pattern recognition |
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Fusion of face and signature at the feature level by using correlation pattern recognition |
title_sort |
fusion of face and signature at the feature level by using correlation pattern recognition |
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2011 |
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http://eprints.utm.my/id/eprint/45885/ http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.294.5629 |
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