Modular Dynamic RBF Neural Network for Face Recognition

被引：0

作者：

Ch'ng, Sue Inn ^{[1
]}

Seng, Kah Phooi ^{[1
]}

Ang, Li-Minn ^{[2
]}

机构：

[1] Sunway Univ, Dept Comp Sci & Networked Syst, Selangor, Malaysia

[2] Edith Cowan Univ, Sch Engn, Churchlands, WA 6018, Australia

来源：

2012 IEEE CONFERENCE ON OPEN SYSTEMS (ICOS 2012) | 2012年

关键词：

modular structure; RBF neural networks; Levenberg-Marquardt algorithm; face recognition; EIGENFACES;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Over the years, we have seen an increase in the use of RBF neural networks for the task of face recognition. However, the use of second order algorithms as the learning algorithm for all the adjustable parameters in such networks are rare due to the high computational complexity of the calculation of the Jacobian and Hessian matrix. Hence, in this paper, we propose a modular structural training architecture to adapt the Levenberg-Marquardt based RBF neural network for the application of face recognition. In addition to the proposal of the modular structural training architecture, we have also investigated the use of different front-end processors to reduce the dimension size of the feature vectors prior to its application to the LM-based RBF neural network. The investigative study was done on three standard face databases; ORL, Yale and AR databases.

引用

页码：133 / 138

页数：6

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