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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wong, Yee Wan Seng, Kah Phooi Ang, Li-Minn Khor, Wan Yong Liau, Fui |
| Copyright Year | 2007 |
| 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. |
| Starting Page | 609 |
| Ending Page | 613 |
| File Size | 447821 |
| Page Count | 5 |
| File Format | |
| ISBN | 0769530729 |
| e-ISBN | 9780769530727 |
| DOI | 10.1109/CIS.2007.196 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-12-15 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Biometrics Face recognition Scattering Feature extraction Robustness Linear discriminant analysis Covariance matrix Mel frequency cepstral coefficient Computational intelligence Principal component analysis |
| Content Type | Text |
| Resource Type | Article |
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