### Abstract

In this paper, we treat the problem of combining fingerprint and speech biometric decisions as a classifier fusion problem. The Feed-forward Neural Network provides a natural choice for such data fusion as it has been shown to be a universal approximator. However, the training process remains much to be a trial-and-error effort since no learning algorithm can guarantee convergence to optimal solution within finite iterations. In this work, we propose a network model to generate different combinations of the hyperbolic functions to achieve some approximation and classification properties. This is to circumvent the iterative training problem as seen in neural networks learning. The proposed hyperbolic functions network model is applied to combine the fingerprint and speaker verification decisions which show either better or comparable results with respect to several commonly used methods.

Original language | English |
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Pages (from-to) | 655-662 |

Number of pages | 8 |

Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |

Volume | 3072 |

Publication status | Published - 2004 Dec 1 |

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### All Science Journal Classification (ASJC) codes

- Theoretical Computer Science
- Computer Science(all)

### Cite this

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*Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)*, vol. 3072, pp. 655-662.

**A hyperbolic function model for multiple biometrics decision fusion.** / Toh, Kar Ann; Jiang, Xudong; Yau, Wei Yun.

Research output: Contribution to journal › Article

TY - JOUR

T1 - A hyperbolic function model for multiple biometrics decision fusion

AU - Toh, Kar Ann

AU - Jiang, Xudong

AU - Yau, Wei Yun

PY - 2004/12/1

Y1 - 2004/12/1

N2 - In this paper, we treat the problem of combining fingerprint and speech biometric decisions as a classifier fusion problem. The Feed-forward Neural Network provides a natural choice for such data fusion as it has been shown to be a universal approximator. However, the training process remains much to be a trial-and-error effort since no learning algorithm can guarantee convergence to optimal solution within finite iterations. In this work, we propose a network model to generate different combinations of the hyperbolic functions to achieve some approximation and classification properties. This is to circumvent the iterative training problem as seen in neural networks learning. The proposed hyperbolic functions network model is applied to combine the fingerprint and speaker verification decisions which show either better or comparable results with respect to several commonly used methods.

AB - In this paper, we treat the problem of combining fingerprint and speech biometric decisions as a classifier fusion problem. The Feed-forward Neural Network provides a natural choice for such data fusion as it has been shown to be a universal approximator. However, the training process remains much to be a trial-and-error effort since no learning algorithm can guarantee convergence to optimal solution within finite iterations. In this work, we propose a network model to generate different combinations of the hyperbolic functions to achieve some approximation and classification properties. This is to circumvent the iterative training problem as seen in neural networks learning. The proposed hyperbolic functions network model is applied to combine the fingerprint and speaker verification decisions which show either better or comparable results with respect to several commonly used methods.

UR - http://www.scopus.com/inward/record.url?scp=35048898880&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=35048898880&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:35048898880

VL - 3072

SP - 655

EP - 662

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

SN - 0302-9743

ER -