Publication:
Design of fisher's linear discriminant (FLD) based pattern classifier for face recognition

dc.contributor.authorKalaai Selvan A/L Muthiah
dc.date.accessioned2025-07-18T03:54:56Z
dc.date.available2025-07-18T03:54:56Z
dc.date.issued2025-07-17
dc.descriptionTA1650 .K34 2012
dc.description.abstractA high-speed method of face recognition using the combination of Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) that is also known as Fisher's Linear Discriminant(FLD) is presented in this thesis. LDA is used to reduce the dimension of the original face in an image and the area illumination variations are lighten. Once the dimension is reduced, PCA is used to find the linear combination of features that have the most total varience in data. Through this a lot of discriminative information will lost when throwing some components away. This actually can produce in bad result especially when it comes to classification. So, in order to find the combination of features that seperates best between classes, Eigenvectors been used as well. This linear projection method FLD is applied further in feature vectors to get the most discriminating and invariant feature of faces.
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/38919
dc.language.isoen
dc.subjectHuman face recognition (Computer science)
dc.subjectPattern recognition systems
dc.titleDesign of fisher's linear discriminant (FLD) based pattern classifier for face recognition
dc.typeResource Types::text::Final Year Project
dspace.entity.typePublication
oaire.citation.endPage70
oaire.citation.startPage1
oairecerif.author.affiliation#PLACEHOLDER_PARENT_METADATA_VALUE#
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