Robust two-dimensional principle component analysis

Author

Chunming, Xu ; Haibo, Jiang ; Jianjiang, Yu

Author_Institution

Sch. of Math. Sci., Yancheng Teachers Univ., Yancheng

fYear

2008

fDate

16-18 July 2008

Firstpage

452

Lastpage

455

Abstract

Two-dimensional principle component analysis (2DPCA) is a popular and fast feature extraction method. However, it doesnpsilat consider the outliers of the training samples. To address this problem, we present robust two-dimensional principle component analysis algorithm (R2DPCA), which gives a new weighted method for the evaluation of the total squared error. The solution for R2DPCA is also given. The proposed method is tested on AR face database and ORL face database, and the experimental results indicate that it is more effective than two-dimensional principle component analysis (2DPCA).

Keywords

feature extraction; principal component analysis; visual databases; AR face database; ORL face database; feature extraction method; robust two-dimensional principle component analysis; Algorithm design and analysis; Face recognition; Feature extraction; Information analysis; Information science; Pareto analysis; Robustness; Spatial databases; Speckle; Testing; Face Recognition; Feature Extraction; Principle Component Analysis (PCA); Robust Two-dimensional Principle Component Analysis (R2DPCA); Two-dimensional Principle Component Analysis (2DPCA);

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference, 2008. CCC 2008. 27th Chinese

Conference_Location

Kunming

Print_ISBN

978-7-900719-70-6

Electronic_ISBN

978-7-900719-70-6

Type

conf

DOI

10.1109/CHICC.2008.4605066

Filename

4605066