Face recognition using RBF neural networks and wavelet transform

Authors:
Bicheng Li;Hujun Yin
Affiliations:
School of Electrical and Electronic Engineering, University of Manchester, Manchester, UK and Information Engineering Institute, Information Engineering University, Zhengzhou, Henan, China;Information Engineering Institute, Information Engineering University, Zhengzhou, Henan, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Year:
2005

Citing 7
Cited 1

A Theory for Multiresolution Signal Decomposition: The Wavelet Representation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Computational Intelligence: An Introduction

Computational Intelligence: An Introduction
Discriminant Waveletfaces and Nearest Feature Classifiers for Face Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces for recognition

Journal of Cognitive Neuroscience
Face recognition using fuzzy Integral and wavelet decomposition method

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

A resource allocating neural network based approach for detecting end-to-end network performance anomaly

ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recently, wavelet transform and image fusion mechanism have been used in face recognition to improve the performance. In this paper, we propose a new face recognition method based on wavelet transform and radial basis function (RBF) fusion network. Firstly, an image is decomposed with wavelet transform (WT) to three levels. Secondly, the Fisherface method is applied to three low-frequency sub-images respectively. Then, the individual classifiers are fused using the RBF neural network. Experimental results show that the proposed method outperforms both individual classifiers and the direct Fisherface method.