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Titolo:
Support vector machines for face recognition
Autore:
Guo, GD; Li, SZ; Chan, KL;
Indirizzi:
Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore Nanyang Technol Univ Singapore Singapore 639798 gapore 639798, Singapore
Titolo Testata:
IMAGE AND VISION COMPUTING
fascicolo: 9-10, volume: 19, anno: 2001,
pagine: 631 - 638
SICI:
0262-8856(20010801)19:9-10<631:SVMFFR>2.0.ZU;2-M
Fonte:
ISI
Lingua:
ENG
Soggetto:
OBJECT RECOGNITION;
Keywords:
face recognition; support vector machines; optimal separating hyperplane; learning networks; binary tree; eigenfaces;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
21
Recensione:
Indirizzi per estratti:
Indirizzo: Guo, GD Microsoft Res Ctr, Beijing Sigma Ctr, 3F,No 49 Zhichun Rd, Beijing100080,Peoples R China Microsoft Res Ctr 3F,No 49 Zhichun Rd Beijing Peoples R China 100080
Citazione:
G.D. Guo et al., "Support vector machines for face recognition", IMAGE VIS C, 19(9-10), 2001, pp. 631-638

Abstract

Support vector machines (SVMs) have been recently proposed as a new learning network for bipartite pattern recognition. In this paper, SVMs incorporated with a binary tree recognition strategy are proposed to tackle the multi-class face recognition problem. The binary tree extends naturally, the pairwise discrimination capability of the SVMs to the multi-class scenario. Two face databases are used to evaluate the proposed method. The performanceof the SVMs based face recognition is compared with the standard eigenfaceapproach, and also the more recently proposed algorithm called the nearestfeature line (NFL). (C) 2001 Elsevier Science B.V. All rights reserved.

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Documento generato il 26/01/20 alle ore 16:11:30