Title :
LNMF Learning for color face recognition
Author :
Bai, Xiaoming ; Wang, Chengzhang
Author_Institution :
Coll. of Inf., Capital Univ. of Econ. & Bus., Beijing, China
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;
Conference_Titel :
Future Computer and Communication (ICFCC), 2010 2nd International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-5821-9
DOI :
10.1109/ICFCC.2010.5497317