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Titolo:
A shape- and texture-based enhanced fisher classifier for face recognition
Autore:
Liu, CJ; Wechsler, H;
Indirizzi:
Univ Missouri, Dept Math & Comp Sci, St Louis, MO 63121 USA Univ MissouriSt Louis MO USA 63121 th & Comp Sci, St Louis, MO 63121 USA George Mason Univ, Dept Comp Sci, Fairfax, VA 22030 USA George Mason UnivFairfax VA USA 22030 pt Comp Sci, Fairfax, VA 22030 USA
Titolo Testata:
IEEE TRANSACTIONS ON IMAGE PROCESSING
fascicolo: 4, volume: 10, anno: 2001,
pagine: 598 - 608
SICI:
1057-7149(200104)10:4<598:ASATEF>2.0.ZU;2-E
Fonte:
ISI
Lingua:
ENG
Soggetto:
AUTOMATIC RECOGNITION; PURSUIT; IMAGES;
Keywords:
enhanced Fisher classifier (EFC); enhanced FLD model (EFM); face recognition; Fisher linear discriminant (FLD); principal component analysis (PCA); shape and texture;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
23
Recensione:
Indirizzi per estratti:
Indirizzo: Liu, CJ Univ Missouri, Dept Math & Comp Sci, St Louis, MO 63121 USA Univ Missouri St Louis MO USA 63121 p Sci, St Louis, MO 63121 USA
Citazione:
C.J. Liu e H. Wechsler, "A shape- and texture-based enhanced fisher classifier for face recognition", IEEE IM PR, 10(4), 2001, pp. 598-608

Abstract

This paper introduces a new face coding and recognition method, the enhanced Fisher classifier (EFC), which employs the enhanced Fisher linear discriminant model (EFM) on integrated shape and texture features. Shape encodes the feature geometry of a face while texture provides a normalized shape-free image. The dimensionalities of the shape and the texture spaces are first reduced using principal component analysis, constrained by the EFM for enhanced generalization. The corresponding reduced shape and texture featuresare then combined through a normalization procedure to form the integratedfeatures that are processed by the EFM for face recognition. Experimental results, using 600 face images corresponding to 200 subjects of varying illumination and facial expressions, show that 1) the integrated shape and texture features carry the most discriminating information followed in order by textures, masked images, and shape images and 2) the new coding and face recognition method, EFC, performs the best among the Eigenfaces method using L-1 or L-2 distance measure, and the Mahalanobis distance classifiers using a common covariance matrix for all classes or a pooled within-class covariance matrix. In particular, EFC achieves 98.5% recognition accuracy usingonly 25 features.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 28/01/20 alle ore 21:09:48