Face recognition based on 2DLDA and support vector machine

Author

Gan, Jun-Ying ; He, Si-Bin

Author_Institution

Sch. of Inf., Wu Yi Univ., Jiangmen, China

fYear

2009

fDate

12-15 July 2009

Firstpage

211

Lastpage

214

Abstract

Singularity problem of LDA algorithm is overcome by two-dimensional LDA (2DLDA), and support vector machine (SVM) has the character of structural risk minimization. In this paper, two methods are combined and used for face recognition. Firstly, the original images are decomposed into high-frequency and low-frequency components with the help of wavelet transform (WT). The high-frequency components are ignored, while the low-frequency components can be obtained. Then, the linear discriminant features are extracted by 2DLDA, and SVM is selected to perform face recognition. Experimental results based on ORL(Olivetti Research Laboratory) and Yale face database show the validity of 2DLDA+SVM for face recognition.

Keywords

face recognition; feature extraction; support vector machines; wavelet transforms; 2DLDA; LDA algorithm; face recognition; linear discriminant feature extraction; support vector machine; wavelet transform; Algorithm design and analysis; Eigenvalues and eigenfunctions; Face recognition; Feature extraction; Linear discriminant analysis; Pattern analysis; Pattern recognition; Risk management; Support vector machines; Wavelet analysis; Face Recognition; Support Vector Machine (SVM); Two-dimensional LDA; Wavelet Transform;

fLanguage

English

Publisher

ieee

Conference_Titel

Wavelet Analysis and Pattern Recognition, 2009. ICWAPR 2009. International Conference on

Conference_Location

Baoding

Print_ISBN

978-1-4244-3728-3

Electronic_ISBN

978-1-4244-3729-0

Type

conf

DOI

10.1109/ICWAPR.2009.5207481

Filename

5207481