A new solution to one sample problem in face recognition using FLDA


APPLIED MATHEMATICS AND COMPUTATION, vol.217, no.24, pp.10368-10376, 2011 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 217 Issue: 24
  • Publication Date: 2011
  • Doi Number: 10.1016/j.amc.2011.05.048
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.10368-10376
  • Keywords: One sample problem, Face recognition, Fisher linear discriminant analysis, QRCP-decomposition, Singular value decomposition, Virtual face image, IMAGE, EIGENFACES
  • Anadolu University Affiliated: Yes


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) [1] in terms of recognition rate and training time in all of the above databases. (C) 2011 Elsevier Inc. All rights reserved.