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Fusion Time Reduction of a Feature Level Based Multimodal Biometric Authentication System
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Author(s): Rasha O. Mahmoud (Department of Computer Science, Faculty of Computers and Informatics, Benha University, Benha, Egypt), Mazen M. Selim (Benha University, Benha, Egypt)and Omar A. Muhi (Department of Computer Science, Faculty of Computers and Informatics, Benha University, Benha, Egypt)
Copyright: 2020
Volume: 12
Issue: 1
Pages: 17
Source title:
International Journal of Sociotechnology and Knowledge Development (IJSKD)
Editor(s)-in-Chief: Lincoln Christopher Wood (University of Otago, New Zealand)and Ahmad Taher Azar (College of Computer & Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia & Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt)
DOI: 10.4018/IJSKD.2020010104
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Abstract
In the present study, a multimodal biometric authentication method is presented to confirm the identity of a person based on his face and iris features. This method depends on multiple biometric techniques that combine face and iris (left and right) features to recognize. The authors have designed and applied a system to identify people. It depends on extracting the features of the face using Rectangle Histogram of Oriented Gradient (R-HOG). The study applies a feature-level fusion using a novel fusion method which employs both the canonical correlation process and the proposed serial concatenation. A deep belief network was used for the recognition process. The performance of the proposed systems was validated and evaluated through a set of experiments on SDUMLA-HMT database. The results were compared with others, and have shown that the fusion time has been reduced by about 34.5%. The proposed system has also succeeded in achieving a lower equal error rate (EER), and a recognition accuracy up to 99%.
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