An Efficient Feature Extraction Method with Pseudo-Zernike Moment in RBF Neural Network-Based Human Face Recognition Sys
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An Efficient Feature Extraction Method with Pseudo-Zernike Moment in RBF Neural Network-Based Human Face Recognition System Javad Haddadnia Engineering Department, Tarbiat Moallem University of Sabzevar, Sabzevar, Khorasan 397, Iran Email: [email protected]
Majid Ahmadi Electrical and Computer Engineering Department, University of Windsor, Windsor, Ontario, Canada N9B 3P4 Email: [email protected]
Karim Faez Electrical Engineering Department, Amirkabir University of Technology, Tehran 15914, Iran Email: [email protected] Received 17 April 2002 and in revised form 24 April 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%. Keywords and phrases: human face recognition, face localization, moment invariant, pseudo-Zernike moment, RBF neural network, learning algorithm.
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INTRODUCTION
Face recognition has been a very popular research topic in recent years because of wide variety of application domains in both academia and industry. This interest is motivated by applications such as access control systems, model-based video coding, image and film processing, criminal identification and authentication in secure systems like computers or bank teller machines, and so forth [1]. A complete face recognition system should include three stages. The first stage is detecting the location of the face, which is difficult because of unknown position, orientation, and scaling of the face in an arbitrary image [2, 3, 4]. The second stage involves extraction of pertinent features from the localized facial image obtained
in the first stage. Finally, the third stage requires classification of facial images based on the derived feature vector obtained in the previous stage. In order to design a high recognition rate system, the choice of feature extractor is very crucial and extraction of pertinent features from two-dimensional images of human face plays an importan
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