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

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العنوان: An Efficient Feature Extraction Method with Pseudo-Zernike Moment in RBF Neural Network-Based Human Face Recognition System
المؤلفون: Ahmadi Majid, Faez Karim, Haddadnia Javad
المصدر: EURASIP Journal on Advances in Signal Processing, Vol 2003, Iss 9, p 267692 (2003)
بيانات النشر: SpringerOpen
سنة النشر: 2003
المجموعة: Directory of Open Access Journals: DOAJ Articles
مصطلحات موضوعية: human face recognition, face localization, moment invariant, pseudo-Zernike moment, RBF neural network, learning algorithm, Telecommunication, TK5101-6720, Electronics, TK7800-8360
الوصف: 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%.
نوع الوثيقة: article in journal/newspaper
اللغة: English
تدمد: 1687-6172
1687-6180
Relation: http://dx.doi.org/10.1155/S1110865703305128; https://doaj.org/toc/1687-6172; https://doaj.org/toc/1687-6180; https://doaj.org/article/1174aa3e02ab4b62b46954450401bfe2
الاتاحة: https://doaj.org/article/1174aa3e02ab4b62b46954450401bfe2
رقم الانضمام: edsbas.D54C81D5
قاعدة البيانات: BASE
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