PCA in wavelet domain for face recognition

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dc.contributor.authorPuyati, Wayo
dc.contributor.authorWalairacht, Somsak
dc.contributor.authorWalairacht, Aranya
dc.date.accessioned2026-08-06T09:54:21Z
dc.date.available2026-08-06T09:54:21Z
dc.date.issued2006-11-17
dc.description.abstractIn this paper, the preprocessing process aimed to reduce size of input image by using wavelet transform before transformed image is sent to the process of PCA for recognition. We used ORL Face Databases from AT&T Laboratories Cambridge in the experiments. The results show that the 4<sup>th</sup> Order Symlets level 2 and level 3 improve the accuracy rate of recognition when compare among Haar wavelets, the 4<sup>th</sup> Order Daubechies wavelets, and Biorthogonal wavelets (orthogonal 6.8). In the case of overall processing time for training, the length of filter of wavelet is directly effect the time consuming. Since LL subband of wavelet decomposition becomes the input for PCA, the memory usage can be greatly reduced.
dc.identifier.citation8th International Conference Advanced Communication Technology Icact 2006 Proceedings, 1, 450-458, 2006
dc.identifier.other2-s2.0-33750955423
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/1359
dc.source8th International Conference Advanced Communication Technology Icact 2006 Proceedings
dc.subjectDimensional reduction
dc.subjectFace recognition
dc.subjectPrincipal component analysis
dc.subjectWavelet transformation
dc.titlePCA in wavelet domain for face recognition
dc.typeConference Paper

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