LNMF Learning for color face recognition

Author

Bai, Xiaoming ; Wang, Chengzhang

Author_Institution

Coll. of Inf., Capital Univ. of Econ. & Bus., Beijing, China

Volume

1

fYear

2010

fDate

21-24 May 2010

Abstract

In this paper, a novel face recognition method named local NMF learning (LNMF Learning) is proposed. Color face is first decomposed into R, G and B components. Component data of the same color channel is aligned together and encoded through matrix mode respectively. Local nonnegative matrix factorization (LNMF) method is employed to compute facial features. Projective coefficients on base images are utilized as features for recognition task. Experimental results on CVL and CMU PIE face databases verify the effectiveness of the proposed approach.

Keywords

face recognition; image colour analysis; matrix decomposition; CMU PIE face databases; CVL face databases; LNMF learning; R-G-B components; color channel; color face recognition; facial features; local nonnegative matrix factorization method; matrix mode; projective coefficients; Educational institutions; Face recognition; Facial features; Feature extraction; Finance; Image converters; Image recognition; Mathematics; Matrix decomposition; Principal component analysis; Face recognition; Feature extraction; Non-negative matrix factorization;

fLanguage

English

Publisher

ieee

Conference_Titel

Future Computer and Communication (ICFCC), 2010 2nd International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-5821-9

Type

conf

DOI

10.1109/ICFCC.2010.5497317

Filename

5497317