Efficient linear discriminant analysis with locality preserving for face recognition

Linear discriminant analysis (LDA) is one of the most popular techniques for extracting features in face recognition. LDA captures the global geometric structure. However, local geometric structure has recently been shown to be effective for face recognition. In this paper, we propose a novel featur...

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Veröffentlicht in:	Pattern recognition Jg. 45; H. 5; S. 1892 - 1898
Hauptverfasser:	Shu, Xin, Gao, Yao, Lu, Hongtao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Kidlington Elsevier Ltd 01.05.2012 Elsevier
Schlagworte:	Algorithms Applied sciences Detection, estimation, filtering, equalization, prediction Discriminant analysis Exact sciences and technology Face recognition Image processing Information, signal and communications theory Least squares method Linear discriminant analysis Locality preserving projection Mathematical models Pattern recognition Regression Signal and communications theory Signal processing Signal, noise Spectra Spectral regression Telecommunications and information theory Tuning Locality preserving projection Linear discriminant analysis Face recognition Spectral regression Biometrics Singularity Discriminant analysis Image processing Model selection Least squares problem Pattern recognition Algorithm Global local method Regularization method Algorithm performance Signal processing Feature extraction Open market Automatic recognition
ISSN:	0031-3203, 1873-5142
Online-Zugang:	Volltext
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