Two-Dimensional Inverse FDA for Face Recognition

Author

Yang, Wankou ; Yan, Hui ; Yin, Jun ; Yang, Jingyu

Author_Institution

Sch. of Comput. Sci. & Technol., Nanjing Univ. of Sci. & Technol., Nanjing

fYear

2008

fDate

22-24 Oct. 2008

Firstpage

1

Lastpage

5

Abstract

In this paper, we propose a two-dimensional Inverse Fisher Discriminant Analysis (2DIFDA) method for feature extraction and face recognition. This method combines the ideas of two-dimensional principal component analysis and Inverse FDA and it can directly extracts the optimal projective vectors from 2D image matrices rather than image vectors based on the inverse fisher discriminant criterion. Experiments on the FERET face databases show that the new method outperforms the PCA , 2DPCA, Fisherfaces and the inverse fisher discriminant analysis.

Keywords

face recognition; feature extraction; principal component analysis; 2D image matrices; 2DPCA; FERET face databases; Fisher discriminant analysis; Fisherfaces; PCA; face recognition; feature extraction; image vectors; optimal projective vector extraction; two-dimensional inverse FDA; two-dimensional principal component analysis; Computer science; Covariance matrix; Data mining; Face recognition; Feature extraction; Image databases; Linear discriminant analysis; Principal component analysis; Scattering; Spatial databases;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 2008. CCPR '08. Chinese Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-2316-3

Type

conf

DOI

10.1109/CCPR.2008.51

Filename

4663004