Fisher linear discriminant analysis (FLDA) is a very popular method in face recognition. But FLDA fails when one image per person is available. This is due to the fact that the within-class scatter matrices cannot be calculated. An image decomposition method that uses QR-decomposition with column pivoting (QRCP) is proposed in this paper to overcome one image per person problem. At first, the image and its two approximations that are evaluated using QRCP-decomposition are all placed in the training set. Then 2D-FLDA method becomes applicable with these new data. The performance of the proposed image decomposition algorithm is tested on five different face databases, namely ORL, FERET, YALE, UMIST, and PolyU-NIR using 2D-FLDA. Our image decomposition algorithm performs better than the SVD based method mentioned by Gao et al. (2008)  in terms of recognition rate and training time in all of the above databases. (C) 2011 Elsevier Inc. All rights reserved.