Two-dimensional neighborhood preserving discriminant analysis for face recognition

Author

Lu, Guanming ; Zuo, Jiakuo

Author_Institution

Coll. of Telecommun. & Inf. Eng., Nanjing Univ. of Posts & Telecommun., Nanjing, China

Volume

7

fYear

2010

fDate

10-12 Aug. 2010

Firstpage

3447

Lastpage

3451

Abstract

In this paper, we propose an innovative feature extraction algorithm named two-dimensional neighborhood preserving discriminant analysis (2DNPDA), which directly extracts feature from image matrix. The proposed algorithm considers both the neighborhood structure of the samples and the discriminant information of different classes. Experimental results on ORL and Yale face databases show that 2DNPDA can attain better recognition rate than PCA, LDA, MMC, 2DPCA, 2DLDA and LPP.

Keywords

face recognition; feature extraction; 2DLDA; 2DPCA; ORL face databases; PCA; Yale face databases; face recognition; image matrix; innovative feature extraction algorithm; linear discriminant analysis; locality preserving projection; maximum margin criterion; two-dimensional neighborhood preserving discriminant analysis; Databases; Face; Face recognition; Feature extraction; Image reconstruction; Principal component analysis; Training; Face recognition; Feature extraction; Linear discriminant analysis (LDA); Locality preserving projection (LPP); Maximum margin criterion (MMC); Two-dimensional neighborhood preserving discriminant analysis (2DNPDA);

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation (ICNC), 2010 Sixth International Conference on

Conference_Location

Yantai, Shandong

Print_ISBN

978-1-4244-5958-2

Type

conf

DOI

10.1109/ICNC.2010.5582718

Filename

5582718