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|Title:||Constructing PCA baseline algorithms to reevaluate ICA-based face-recognition performance|
Zhang, David D.
Independent component analysis (ICA)
Principal component analysis (PCA)
|Source:||IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics, Aug. 2007, v. 37, no. 4, p.1015-1021.|
|Abstract:||The literature on independent component analysis (ICA)-based face recognition generally evaluates its performance using standard principal component analysis (PCA) within two architectures, ICA Architecture I and ICA Architecture II. In this correspondence, we analyze these two ICA architectures and find that ICA Architecture I involves a vertically centered PCA process (PCA I), while ICA Architecture II involves a whitened horizontally centered PCA process (PCA II). Thus, it makes sense to use these two PCA versions as baselines to reevaluate the performance of ICA-based face-recognition systems. Experiments on the FERET, AR, and AT&T face-image databases showed no significant differences between ICA Architecture I (II) and PCA I (II), although ICA Architecture I (or II) may, in some cases, significantly outperform standard PCA. It can be concluded that the performance of ICA strongly depends on the PCA process that it involves. Pure ICA projection has only a trivial effect on performance in face recognition.|
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|Appears in Collections:||COMP Journal/Magazine Articles|
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