Open Access

An Efficient Feature Extraction Method with Pseudo-Zernike Moment in RBF Neural Network-Based Human Face Recognition System

EURASIP Journal on Advances in Signal Processing20032003:267692

DOI: 10.1155/S1110865703305128

Received: 17 April 2002

Published: 18 August 2003


This paper introduces a novel method for the recognition of human faces in digital images using a new feature extraction method that combines the global and local information in frontal view of facial images. Radial basis function (RBF) neural network with a hybrid learning algorithm (HLA) has been used as a classifier. The proposed feature extraction method includes human face localization derived from the shape information. An efficient distance measure as facial candidate threshold (FCT) is defined to distinguish between face and nonface images. Pseudo-Zernike moment invariant (PZMI) with an efficient method for selecting moment order has been used. A newly defined parameter named axis correction ratio (ACR) of images for disregarding irrelevant information of face images is introduced. In this paper, the effect of these parameters in disregarding irrelevant information in recognition rate improvement is studied. Also we evaluate the effect of orders of PZMI in recognition rate of the proposed technique as well as RBF neural network learning speed. Simulation results on the face database of Olivetti Research Laboratory (ORL) indicate that the proposed method for human face recognition yielded a recognition rate of 99.3%.


human face recognition face localization moment invariant pseudo-Zernike moment RBF neural network learning algorithm

Authors’ Affiliations

Engineering Department, Tarbiat Moallem University of Sabzevar
Electrical and Computer Engineering Department, University of Windsor
Electrical Engineering Department, Amirkabir University of Technology


© Copyright © 2003 Hindawi Publishing Corporation 2003