Abstract
In this paper, a new multimodal biometric recognition system based on feature fusion is proposed to increase the robustness and circumvention of conventional multimodal recognition system. The feature sets originating from the output of the visual and audio feature extraction systems are fused and being classified by RBF neural network. Other than that, 2DPCA is proposed to work in conjunction with LDA to further increase the recognition performance of the visual recognition system. The experimental result shows that the proposed system achieves a higher recognition rate as compared to the conventional multimodal recognition system. Besides, we also show that the 2DPCA +LDA achieves a higher recognition rate as compared with PCA, PCA+LDA and 2DPCA.
Original language | English |
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Title of host publication | Proceedings - 2007 International Conference on Computational Intelligence and Security, CIS 2007 |
Pages | 609-613 |
Number of pages | 5 |
DOIs | |
Publication status | Published - 2007 |
Externally published | Yes |
Event | 2007 International Conference on Computational Intelligence and Security, CIS'07 - Harbin, Heilongjiang, China Duration: 15 Dec 2007 → 19 Dec 2007 |
Publication series
Name | Proceedings - 2007 International Conference on Computational Intelligence and Security, CIS 2007 |
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Conference
Conference | 2007 International Conference on Computational Intelligence and Security, CIS'07 |
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Country/Territory | China |
City | Harbin, Heilongjiang |
Period | 15/12/07 → 19/12/07 |