Modified algorithm of two-dimension LDA for face recognition

Author

Wang, Yanjiang ; Fang, Juan ; Suo, Peng

Author_Institution

Coll. of Inf. & Control Eng., China Univ. of Pet., Dongying

fYear

2008

Firstpage

1524

Lastpage

1527

Abstract

A modified two-dimension linear discriminant analysis (2DLDA) algorithm is proposed. After lighting compensation, the input face images are processed first by singular value decomposition (SVD) perturbation and wavelet transform, and then the 2DLDA is used to extract the features, whose dimension is further reduced by PCA algorithm. At last, the support vector machines (SVM) classifier is used for classification and recognition. Experimental results on ORL database show that the proposed method has better recognition performance than other PCA- and LDA-based algorithms.

Keywords

face recognition; feature extraction; image classification; principal component analysis; singular value decomposition; support vector machines; wavelet transforms; 2D linear discriminant analysis; PCA algorithm; face recognition; feature extraction; lighting compensation; singular value decomposition perturbation; support vector machine classifier; wavelet transform; Face recognition; Feature extraction; Image databases; Linear discriminant analysis; Principal component analysis; Singular value decomposition; Spatial databases; Support vector machine classification; Support vector machines; Wavelet transforms;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing, 2008. ICSP 2008. 9th International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-2178-7

Electronic_ISBN

978-1-4244-2179-4

Type

conf

DOI

10.1109/ICOSP.2008.4697423

Filename

4697423