POTENTIALS OF ONE STEP SECANT AND FLETCHER-POWELL CONJUGATE GRADIENT BACK PROPAGATION ALGORITHMS FOR MULTIMODAL BIOMETRIC RECOGNITION APPLICATIONS
- Authors
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Esan, O. A
The Federal University of Technology, Akure, Ondo State, Nigeria
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Adedeji, K. B
The Federal University of Technology, Akure, Ondo State, Nigeria
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Itodo, E. S.
The Federal University of Technology, Akure, Ondo State, Nigeria
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- Keywords:
- Array, Array, Array, Array, Array, Array
- Abstract
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There has been an increased interest in reliable methods of biometric identification due to the growing emphasis on security and the increasing prevalence of identity theft. A multimodal system which combines several physiological traits of an individual could improve the security level. Most commercial biometric systems use artificial neural network (ANN) techniques due to their robustness to parameter changes. The performance of such a system depends on the learning algorithm. In this paper, the potential of Fletcher Powell Conjugate Gradient (FPCG) and One Step Secant (OSS) backpropagation learning algorithms for a multimodal hand geometry recognition application is considered and analysed. The effectiveness of these algorithms is assessed using the mean square error, image gradient, error histogram, and regression. Simulation results show that the FPCG algorithm has better performance than the OSS algorithm in terms of the mean square error, regression, and image gradient. The mean square error due to the FPCG is lower than that of the OSS algorithm. This indicates that there is about 76% improvement in the mean square error if the FPCG algorithm is selected over the OSS. Also, the regression analysis shows that the FPCG algorithm is more accurate than the OSS algorithm. However, in terms of speed of convergence, the OSS algorithm has the lowest number of epochs to attain best performance. Among these two backpropagation algorithms, the FPCG is considered more suitable for multimodal hand geometry identification.
- Author Biographies
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