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Please use this identifier to cite or link to this item: https://ptsldigital.ukm.my/jspui/handle/123456789/578197
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dc.contributor.authorAli Garba Garba (UKM)
dc.contributor.authorJamilu Awwalu (UKM)
dc.contributor.authorHaslina Arshad (UKM)
dc.contributor.authorLailatul Qadri Zakaria (UKM)
dc.date.accessioned2023-11-06T02:59:07Z-
dc.date.available2023-11-06T02:59:07Z-
dc.date.issued2017-06
dc.identifier.issn0128-7680
dc.identifier.otherukmvital:113379
dc.identifier.urihttps://ptsldigital.ukm.my/jspui/handle/123456789/578197-
dc.descriptionFace detection and analysis is an important area in computer vision. Furthermore, face detection has been an active research field in the recent years following the advancement in digital image processing. The visualisation of visual entities or sub-pattern composition may become complex to visualise due to the high frequency of noise and light effect during examination. This study focuses on evaluating the ability of Haar classifier in detecting faces from three paired Min-Max values used on histogram stretching. Min-Max histogram stretching was the selected method for implementation given that it appears to be the appropriate technique from the observation carried out. Experimental results show that, 60-240 Min-Max values, Haar classifier can accurately detect faces compared to the two values.
dc.language.isoen
dc.publisherUniversiti Putra Malaysia Press
dc.relation.haspartPertanika Journals
dc.relation.urihttp://www.pertanika.upm.edu.my/view_archives.php?journal=JST-25-S-6
dc.rightsUKM
dc.subjectFace detection
dc.subjectHaar classifier
dc.subjectNormalisation
dc.titlePerformance comparison of min-max normalisation on frontal face detection using haar classifiers
dc.typeJournal Article
dc.format.volume25
dc.format.pages163-172
dc.format.issueSpecial Issue
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