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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Khan, W. Delmas, P. Gimel'farb, G. Morris, J. |
| Copyright Year | 2008 |
| Description | Author affiliation: Dept. of Comput. Sci., Univ. of Auckland, Auckland (Khan, W.; Delmas, P.; Gimel'farb, G.; Morris, J.) |
| Abstract | Methods of representing images by projection onto a linear subspace, such as Principal Component Analysis (PCA), 2-Dimensional PCA (2D PCA), and two variants of robust Linear Discriminant Analysis (RLDA) using PCA to refine the chosen subspace, are compared by the accuracy of linear and nearest neighbour classification of the reduced and original image vectors. Experiments with the Olivetti-ATT Database of Faces and the Face Expression Database have shown that the RLDA with an extended subspace outperforms the other methods. |
| Starting Page | 1 |
| Ending Page | 6 |
| File Size | 455636 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424437801 |
| DOI | 10.1109/IVCNZ.2008.4762102 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-11-26 |
| Publisher Place | New Zealand |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Face recognition within-class scatter matrix Scattering robust LDA LDA Vectors Covariance matrix Linear subspace Image reconstruction PCA Image databases Bismuth face recognition Robustness Linear discriminant analysis Principal component analysis |
| Content Type | Text |
| Resource Type | Article |
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