Essence of 2DPCA and Modification Method for Face Recognition

Author

Hou, Jun ; Gao, Quan-xue ; Pan, Quan ; Zhang, Hong-cai

Author_Institution

Coll. of Autom., Northwestern Polytech. Univ., Xi´´an

fYear

2006

fDate

13-16 Aug. 2006

Firstpage

3351

Lastpage

3353

Abstract

In this paper, the method of 2DPCA is analyzed and its nature is revealed, i.e., 2DPCA is equivalent to view rows of face images as training samples that constitute row training sets and then use PCA for feature extraction. We also have proved that principal component vectors extracted by 2DPCA contain redundancy in theory. Based on this result, this paper presents a new image feature extraction method. The proposed method provides a sequentially optimal image compression mechanism. Finally, the effectiveness of the proposed algorithm is verified using the ORL database

Keywords

data compression; face recognition; feature extraction; image coding; principal component analysis; vectors; 2DPCA; ORL database; face recognition; image compression mechanism; image feature extraction method; principal component analysis; principal component vectors; Automation; Covariance matrix; Cybernetics; Educational institutions; Face recognition; Feature extraction; Image analysis; Image databases; Image recognition; Machine learning; Principal component analysis; Spatial databases; 2DPCA; Face recognition; Principal component analysis (PCA);

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2006 International Conference on

Conference_Location

Dalian, China

Print_ISBN

1-4244-0061-9

Type

conf

DOI

10.1109/ICMLC.2006.258473

Filename

4028646