Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Information fusion in biometrics
Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Speaker recognition with a MLP classifier and LPCC codebook
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
Evaluation and analysis of a face and voice outdoor multi-biometric system
Pattern Recognition Letters
Biometric scores fusion based on total error rate minimization
Pattern Recognition
Score normalization in multimodal biometric systems
Pattern Recognition
How to handle missing data in robust multi-biometrics verification
International Journal of Biometrics
Computers in Biology and Medicine
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Biometric solution for embedded device gained significant attention in the commercial and research sectors over recent years. Combining multiple biometrics may enhance the performance of personal verification system in accuracy and reliability. This paper presents a new multi-biometric verification solution aimed at implementing on an embedded system within a wide range of applications. The system combines the voiceprint and fingerprint biometrics and makes decision at score level. Fusion strategy is based on score normalization and support vector machine (SVM) classifier. We tested the performances of SVM using three kernel functions for system adaptation. Experimental result demonstrates that proposed multibiometric verification approach achieves 1.0067% equal error rate (EER) that can be deployed in majority of embedded devices such as PDA and smart cell phone for user's identity verification.