Face Recognition using PCA on Enhanced Image for Single Training Images

Author

He, Jia-Zhong ; Zhu, Qing-huan ; Du, Ming-hui

Author_Institution

Sch. of Inf. Eng., Shaoguan Coll.

fYear

2006

fDate

13-16 Aug. 2006

Firstpage

3218

Lastpage

3221

Abstract

An image enhancement based principal component analysis (PCA) method is proposed to deal with face recognition with single training image per person. The method combines the original training image is with its reconstructed image using only a few low-frequency discrete cosine transform (DCT) coefficients and then performs PCA on the enhanced training images set. In comparison with the standard eigenface algorithm and recent single training image based extended eigenface algorithms on ORL face database, the proposed method shows an improvement of more than 6% in recognition accuracy

Keywords

discrete cosine transforms; face recognition; image enhancement; image reconstruction; principal component analysis; ORL face database; PCA; eigenface algorithm; face recognition; image enhancement; image reconstruction; low-frequency discrete cosine transform coefficients; principal component analysis; single training images; Cybernetics; Discrete cosine transforms; Eyes; Face detection; Face recognition; Facial features; Feature extraction; Humans; Image recognition; Image reconstruction; Lighting; Machine learning; Mouth; Principal component analysis; Face recognition; discrete cosine transform (DCT); eigenface; 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.2006.258429

Filename

4028621