Mathematical Problems in Engineering
Volume 2008 (2008), Article ID 410674, 17 pages
doi:10.1155/2008/410674
Research Article
Incremental Nonnegative Matrix Factorization for Face Recognition
1College of Mathematics and Computational Science, Shenzhen University, Shenzhen 518060, China
2College of Computer Science, Chongqing University, Chongqing 400044, China
3School of Information Science & Technology, East China Normal University, Shanghai 200241, China
Received 25 May 2008; Accepted 5 June 2008
Academic Editor: Cristian Toma
Copyright © 2008 Wen-Sheng Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Nonnegative matrix factorization (NMF) is a promising approach for local feature
extraction in face recognition tasks. However, there are two major drawbacks in almost all existing
NMF-based methods. One shortcoming is that the computational cost is expensive for large matrix
decomposition. The other is that it must conduct repetitive learning, when the training samples or
classes are updated. To overcome these two limitations, this paper proposes a novel incremental
nonnegative matrix factorization (INMF) for face representation and recognition. The proposed INMF
approach is based on a novel constraint criterion and our previous block strategy. It thus has some
good properties, such as low computational complexity, sparse coefficient matrix. Also, the
coefficient column vectors between different classes are orthogonal. In particular, it can be applied
to incremental learning. Two face databases, namely FERET and CMU PIE face databases, are
selected for evaluation. Compared with PCA and some state-of-the-art NMF-based methods, our
INMF approach gives the best performance.