Efficiency improvement for unconstrained face recognition by weightening probability values of modular PCA and Wavelet PCA

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dc.contributor.authorPuyati, Wayo
dc.contributor.authorWalairacht, Aranya
dc.date.accessioned2026-08-06T09:56:42Z
dc.date.available2026-08-06T09:56:42Z
dc.date.issued2008-05-29
dc.description.abstractPrincipal Component Analysis (PCA) is a well-known classical appearance-base method in face recognition. In the previous works, the preprocessing process significantly improved the recognition rate. Modular PCA and Wavelet PCA are the preprocessing processes of PCA, which increase the recognition rate of the original PCA. Modular PCA is suitable for the highvaried face database, while Wavelet PCA for the low-varied face database. In this paper, we propose the preprocessing method which combines between Modular PCA and Wavelet PCA with the weightening probability values. The experiments are compared among our propose method, Modular PCA, Wavelet PCA and original PCA with face database from Yale, ORL and UMIST. The experimental results show that the recognition rate of our method is higher compared to the other methods and also support variety of face database.
dc.identifier.citationInternational Conference on Advanced Communication Technology Icact, 2, 1449-1453, 2008
dc.identifier.doi10.1109/ICACT.2008.4494037
dc.identifier.issn17389445
dc.identifier.other2-s2.0-44249120607
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/2032
dc.sourceInternational Conference on Advanced Communication Technology Icact
dc.subjectModular PCA
dc.subjectModular PCA and Wavelet PCA combination
dc.subjectPrincipal component analysis
dc.subjectUnconstrained face recognition
dc.subjectWavelet PCA
dc.titleEfficiency improvement for unconstrained face recognition by weightening probability values of modular PCA and Wavelet PCA
dc.typeConference Paper

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