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Mathematical Problems in Engineering
Volume 2015, Article ID 523603, 16 pages
Research Article

Face Recognition Using MLP and RBF Neural Network with Gabor and Discrete Wavelet Transform Characterization: A Comparative Study

Speech Communication and Signal Processing Laboratory, Faculty of Electronics and Computer Science, University of Science and Technology Houari Boumedienne (USTHB), P.O. Box 32, 16111 Algiers, Algeria

Received 13 September 2014; Revised 1 December 2014; Accepted 16 December 2014

Academic Editor: Marco Pérez-Cisneros

Copyright © 2015 Fatma Zohra Chelali and Amar Djeradi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Face recognition has received a great attention from a lot of researchers in computer vision, pattern recognition, and human machine computer interfaces in recent years. Designing a face recognition system is a complex task due to the wide variety of illumination, pose, and facial expression. A lot of approaches have been developed to find the optimal space in which face feature descriptors are well distinguished and separated. Face representation using Gabor features and discrete wavelet has attracted considerable attention in computer vision and image processing. We describe in this paper a face recognition system using artificial neural networks like multilayer perceptron (MLP) and radial basis function (RBF) where Gabor and discrete wavelet based feature extraction methods are proposed for the extraction of features from facial images using two facial databases: the ORL and computer vision. Good recognition rate was obtained using Gabor and DWT parameterization with MLP classifier applied for computer vision dataset.