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Titolo:
Combined classifiers for invariant face recognition
Autore:
Tolba, AS; Abu-Rezq, AN;
Indirizzi:
Kuwait Univ, Dept Phys, Safat 13060, Kuwait Kuwait Univ Safat Kuwait 13060 wait Univ, Dept Phys, Safat 13060, Kuwait
Titolo Testata:
PATTERN ANALYSIS AND APPLICATIONS
fascicolo: 4, volume: 3, anno: 2000,
pagine: 289 - 302
SICI:
1433-7541(2000)3:4<289:CCFIFR>2.0.ZU;2-H
Fonte:
ISI
Lingua:
ENG
Soggetto:
NEURAL-NETWORK;
Keywords:
classification; combined classifiers; invariant recognition; face recognition; learning vector quantisation; radial basis function network;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
34
Recensione:
Indirizzi per estratti:
Indirizzo: Tolba, AS Kuwait Univ, Dept Phys, POB 5969, Safat 13060, Kuwait Kuwait Univ POB 5969 Safat Kuwait 13060 69, Safat 13060, Kuwait
Citazione:
A.S. Tolba e A.N. Abu-Rezq, "Combined classifiers for invariant face recognition", PATTERN A A, 3(4), 2000, pp. 289-302

Abstract

This paper presents a system for invariant face recognition. A combined classifier uses the generalisation capabilities of both Learning Vector Quantisation (LVQ) and Radial Basis Function (RBF) neural networks to build a representative model of a face from a variety of training patterns with different poses, details and facial expressions. The combined generalisation error of the classifier is found to be lower than that of each individual classifier. A new face synthesis method is implemented for reducing the false acceptance rate and enhancing the rejection capability of the classifier. The system is capable of recognising a face in less than one second. The well-known ORL database is used for testing the combined classifier. Comparisons with several other systems show that our system compares favourably with the state-of-the-art systems. In the case of the ORL database, a correct recognition of 99.5% at 0.5% rejection rate is achieved.

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Documento generato il 22/01/20 alle ore 18:26:53