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

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• 作者：Mehmet Koç ; ; ^a ; ^{mkoc6@anadolu.edu.tr"" rel=""nofollow} ; ^{mehmetkoc12@gmail.com"" rel=""nofollow} ; Atalay Barkana^a
• 关键词：One sample problem ; Face recognition ; Fisher linear discriminant analysis ; QRCP-decomposition ; Singular value decomposition ; Virtual face image
• 刊名：Applied Mathematics and Computation
• 出版年：2011
• 出版时间：15 August 2011
• 年：2011
• 卷：217
• 期：24
• 页码：10368-10376
• 全文大小：840 K

文摘

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.