BDPCA Plus LDA: A Novel Fast Feature Extraction Technique for Face Recognition

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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 sub spaces may be more effective. In this correspondence,we proposed an ovel fast feature extraction technique, bidirectional PCA (BDPCA) plus LDA (BDPCA + LDA), whichperforms 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

3 years ago


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