Mathematical Problems in Engineering
Volume 2008 (2008), Article ID 410674, 17 pages
Research Article

Incremental Nonnegative Matrix Factorization for Face Recognition

Wen-Sheng Chen,1 Binbin Pan,1 Bin Fang,2 Ming Li,3 and Jianliang Tang1

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.


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.