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Please use this identifier to cite or link to this item: https://ptsldigital.ukm.my/jspui/handle/123456789/576823
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dc.contributor.authorTherry Z. Lee (UMS)
dc.contributor.authorDavid B. L. Bong (UMS)
dc.date.accessioned2023-11-06T02:08:13Z-
dc.date.available2023-11-06T02:08:13Z-
dc.identifier.issn0128-7680
dc.identifier.otherukmvital:82494
dc.identifier.urihttps://ptsldigital.ukm.my/jspui/handle/123456789/576823-
dc.descriptionThe bit-plane feature extraction approach has lately been introduced for face and palm-print recognition. This approach decomposes an 8-bit grey level image into eight groups of bit layers. The assumption of this approach is that the highest order of a bit-plane decomposition, which has the most significant bits of all pixels, contains the most biometric features. Nonetheless, most research has identified bit-plane images illustratively. Hence, in order to endorse the assumption, we performed an analysis on face and palm-print images to identify the bit-plane that contributes most significantly to the recognition performance. Analysis was done based on Principal Component Analysis (PCA). The first principal component was applied as it is defined for the largest possible variance of the data. Next, Euclidean distance was calculated for matching performance. It was observed that bit-plane 6 and 7 contributed significantly to recognition performance.
dc.language.isoen
dc.publisherUniversiti Putra Malaysia
dc.relation.haspartPertanika Journals
dc.relation.urihttp://www.pertanika.upm.edu.my/regular_issues.php?jtype=2&journal=JST-24-1-1
dc.subjectBit-plane
dc.subjectPrincipal Component Analysis
dc.subjectFace recognition
dc.subjectPalm-print recognition
dc.titleAnalysis of bit-plane images by using principal component on face and palmprint database
dc.typeJournal Article
dc.format.volume24
dc.format.pages191-203
dc.format.issue1
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