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|Title: ||BDPCA plus LDA : a novel fast feature extraction technique for face recognition|
|Authors: ||Zuo, Wangmeng|
Zhang, David D.
|Subjects: ||Bidirectional principal component analysis (BDPCA)|
Linear discriminant analysis (LDA)
Principal component analysis (PCA)
|Issue Date: ||Aug-2006 |
|Citation: ||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.|
|Rights: ||© 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
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|Type: ||Journal/Magazine Article|
|Appears in Collections:||COMP Journal/Magazine Articles|
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