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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Abstract	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 feature extraction algorithm which integrates both global and local geometric structures. We first cast LDA as a least square problem based on the spectral regression, then regularization technique is used to model the global and local geometric structures. Furthermore, we impose penalty on parameters to tackle the singularity problem and design an efficient model selection algorithm to choose the optimal tuning parameter which balances the tradeoff between the global and local structures. Experimental results on four well-known face data sets show that the proposed integration framework is competitive with traditional face recognition algorithms, which use either global or local structure only. ► We proposed a regularized least squares LDA which integrates both the global and local structures for face recognition. ► The formulation of regularized least squares LDA is based on spectral regression. ► The local structure is modeled via a regularization term defined by the graph Laplacian. ► We design an efficient algorithm for the estimation of the optimal tuning parameter.
AbstractList	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 feature extraction algorithm which integrates both global and local geometric structures. We first cast LDA as a least square problem based on the spectral regression, then regularization technique is used to model the global and local geometric structures. Furthermore, we impose penalty on parameters to tackle the singularity problem and design an efficient model selection algorithm to choose the optimal tuning parameter which balances the tradeoff between the global and local structures. Experimental results on four well-known face data sets show that the proposed integration framework is competitive with traditional face recognition algorithms, which use either global or local structure only. 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 feature extraction algorithm which integrates both global and local geometric structures. We first cast LDA as a least square problem based on the spectral regression, then regularization technique is used to model the global and local geometric structures. Furthermore, we impose penalty on parameters to tackle the singularity problem and design an efficient model selection algorithm to choose the optimal tuning parameter which balances the tradeoff between the global and local structures. Experimental results on four well-known face data sets show that the proposed integration framework is competitive with traditional face recognition algorithms, which use either global or local structure only. ► We proposed a regularized least squares LDA which integrates both the global and local structures for face recognition. ► The formulation of regularized least squares LDA is based on spectral regression. ► The local structure is modeled via a regularization term defined by the graph Laplacian. ► We design an efficient algorithm for the estimation of the optimal tuning parameter.
Author	Shu, Xin Lu, Hongtao Gao, Yao
Author_xml	– sequence: 1 givenname: Xin surname: Shu fullname: Shu, Xin email: shupan1984@hotmail.com – sequence: 2 givenname: Yao surname: Gao fullname: Gao, Yao – sequence: 3 givenname: Hongtao surname: Lu fullname: Lu, Hongtao
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Keywords	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
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Snippet	Linear discriminant analysis (LDA) is one of the most popular techniques for extracting features in face recognition. LDA captures the global geometric...
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SubjectTerms	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
Title	Efficient linear discriminant analysis with locality preserving for face recognition
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