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

作者：Koc Mehmet^*; Barkana Atalay

来源：Applied Mathematics and Computation, 2011, 217(24): 10368-10376.

DOI：10.1016/j.amc.2011.05.048

• One sample problem
• Face recognition
• Fisher linear discriminant analysis
• QRCP-decomposition
• Singular value decomposition
• Virtual face image

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.

• 出版日期2011-8-15