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|Title:||BDPCA plus LDA : a novel fast feature extraction technique for face recognition|
Zhang, David D.
|Subjects:||Bidirectional principal component analysis (BDPCA)|
Linear discriminant analysis (LDA)
Principal component analysis (PCA)
|Source:||IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics, Aug. 2006, v. 36, no. 4, p. 946-953.|
|Abstract:||Appearance-based methods, especially linear discriminant analysis (LDA), have been very successful in facial feature extraction, but the recognition performance of LDA is often degraded by the so-called “small sample size” (SSS) problem. One popular solution to the SSS problem is principal component analysis (PCA) + LDA (Fisherfaces), but the LDA in other low-dimensional subspaces may be more effective. In this correspondence, we proposed a novel fast feature extraction technique, bidirectional PCA (BDPCA) plus LDA (BDPCA + LDA), which performs an LDA in the BDPCA subspace. Two face databases, the ORL and the Facial Recognition Technology (FERET) databases, are used to evaluate BDPCA + LDA. Experimental results show that BDPCA + LDA needs less computational and memory requirements and has a higher recognition accuracy than PCA + LDA.|
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|Appears in Collections:||COMP Journal/Magazine Articles|
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