Face Recognition System using Discrete Cosine Transform combined with MLP and RBF Neural Networks

Authors:
Fatma Zohra Chelali;Amar Djeradi
Affiliations:
Speech Communication and Signal Processing Laboratory, Houari Boumedienne University of Sciences and Technologies, El Alia, Algeria;Speech Communication and Signal Processing Laboratory, Houari Boumedienne University of Sciences and Technologies, El Alia, Algeria
Venue:
International Journal of Mobile Computing and Multimedia Communications
Year:
2012

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Abstract

Proposed is an efficient face recognition algorithm using the discrete cosine transform DCT Technique for reducing dimensionality and image parameterization. These DCT coefficients are examined by a MLP Multi-Layer Perceptron and radial basis function RBF neural networks. Their purpose is to present a face recognition system that is a combination of discrete cosine transform DCT algorithm with a MLP and RBF neural networks. Neural networks have been widely applied in pattern recognition for the reason that neural-networks-based classifiers can incorporate both statistical and structural information and achieve better performance than the simple minimum distance classifiers. The authors demonstrate experimentally that when DCT coefficients are fed into a back propagation neural network for classification, a high recognition rate can be achieved by using a very small proportion of transform coefficients. Comparison with other statistical methods like Principal component Analysis PCA and Linear Discriminant Analysis LDA is presented. Their face recognition system is tested on the computer vision science research projects and the ORL database.