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Titolo:
Topological principal component analysis for face encoding and recognition
Autore:
Pujol, A; Vitria, J; Lumbreras, F; Villanueva, JJ;
Indirizzi:
Univ Autonoma Barcelona, Comp Vis Lab, Bellaterra 08193, Cerdanyola Vall, Spain Univ Autonoma Barcelona Bellaterra Cerdanyola Vall Spain 08193 all, Spain Univ Autonoma Barcelona, Dept Informat, Bellaterra 08193, Cerdanyola Vall,Spain Univ Autonoma Barcelona Bellaterra Cerdanyola Vall Spain 08193 Vall,Spain
Titolo Testata:
PATTERN RECOGNITION LETTERS
fascicolo: 6-7, volume: 22, anno: 2001,
pagine: 769 - 776
SICI:
0167-8655(200105)22:6-7<769:TPCAFF>2.0.ZU;2-3
Fonte:
ISI
Lingua:
ENG
Keywords:
generalization; principal component analysis; face recognition; topological covariance matrix; covariance estimation;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
17
Recensione:
Indirizzi per estratti:
Indirizzo: Pujol, A Univ Autonoma Barcelona, Comp Vis Lab, Edif O, Bellaterra 08193, Cerdanyola Vall, Spain Univ Autonoma Barcelona Edif O Bellaterra CerdanyolaVall Spain 08193
Citazione:
A. Pujol et al., "Topological principal component analysis for face encoding and recognition", PATT REC L, 22(6-7), 2001, pp. 769-776

Abstract

Principal component analysis (PCA)-like methods make use of an estimation of the covariances between sample variables. This estimation does not take into account their topological relationships. This paper proposes how to use these relationships in order to estimate the covariances in a more robustway. The new method topological principal component analysis (TPCA) is tested using both face encoding and recognition experiments showing how the generalization capabilities of PCA are improved. (C) 2001 Elsevier Science B.V. All rights reserved.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 22/01/20 alle ore 12:17:56