Catalogo Articoli (Spogli Riviste)

OPAC HELP

Titolo:
Face recognition based on the uncorrelated discriminant transformation
Autore:
Jin, Z; Yang, JY; Hu, ZS; Lou, Z;
Indirizzi:
Nanjing Univ Sci & Technol, Dept Comp Sci, Nanjing 210094, Peoples R ChinaNanjing Univ Sci & Technol Nanjing Peoples R China 210094 eoples R China
Titolo Testata:
PATTERN RECOGNITION
fascicolo: 7, volume: 34, anno: 2001,
pagine: 1405 - 1416
SICI:
0031-3203(200107)34:7<1405:FRBOTU>2.0.ZU;2-8
Fonte:
ISI
Lingua:
ENG
Soggetto:
NEURAL-NETWORK; CLASSIFIERS;
Keywords:
pattern recognition; feature extraction; discriminant analysis; dimensionality reduction; face recognition;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
18
Recensione:
Indirizzi per estratti:
Indirizzo: Jin, Z Nanjing Univ Sci & Technol, Dept Comp Sci, Nanjing 210094, Peoples R China Nanjing Univ Sci & Technol Nanjing Peoples R China 210094 R China
Citazione:
Z. Jin et al., "Face recognition based on the uncorrelated discriminant transformation", PATT RECOG, 34(7), 2001, pp. 1405-1416

Abstract

The extraction of discriminant features is the most fundamental and important problem in face recognition. This paper presents a method to extract optimal discriminant features for face images by using the uncorrelated discriminant transformation and It L expansion. Experiments on the ORL database and the NUST603 database have been performed. Experimental results show that the uncorrelated discriminant transformation is superior to the Foley-Sammon discriminant transformation and the new method to extract uncorrelated discriminant features for face images is very effective. An error late of 2.5% ig obtained with the experiments on the ORL database. An average error rate of 1.2% is obtained with the experiments on the NUST603 database. Experiments show that by extracting uncorrelated discriminant features, face recognition could be performed with higher accuracy on lower than 16 x 16 resolution mosaic images. It is suggested that for the uncorrelated discriminant transformation, the optimal face image resolution can be regarded as theresolution m x n which makes the dimensionality N = mn of the original image vector space be larger and closer to the number of known-face classes. (C) 2001 pattern Recognition Society. Published by Elsevier Science Ltd. Allrights reserved.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 19/01/20 alle ore 20:42:49