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

Fatma Zohra Chelali,Amar Djeradi

doi:10.1155/2015/523603

Abstract

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.

Highlights

Face recognition has received a great attention from a lot of scientists in computer vision, pattern recognition, and human machine computer interfaces in recent years.Face is the most common biometric identifier used by humans; this domain is motivated by the increased interest in the commercial applications of automatic face recognition (AFR) as well as by the emergence of real-time processors
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 Olivetti Research Laboratory (ORL) and computer vision
Simulation results showed the efficiency of the proposed method; about 94 to 96% was obtained with Gabor filters and MLP/RBF classifier for computer vision database, whereas about 85% was obtained for ORL dataset

Summary

Introduction

Face recognition has received a great attention from a lot of scientists in computer vision, pattern recognition, and human machine computer interfaces in recent years. Face is the most common biometric identifier used by humans; this domain is motivated by the increased interest in the commercial applications of automatic face recognition (AFR) as well as by the emergence of real-time processors. Automatic face recognition (FR) is one of the most visible and challenging research topics in computer vision, machine learning, and biometrics [1]. Face recognition has become important because of the potential value for applications and its theoretical challenges. Face recognition technology is being used to combat passport fraud, support law enforcement, identify missing children, and identify fraud. There are so many challenges in face recognition; some of them are (1) illumination variances, (2) occlusions, and (3) different expressions and poses [2]

Methods

Results

Discussion

Conclusion