A comparison study on PCA, modular PCA and LDA for face recognition

dc.contributor.author	Cheah, Boon Wah
dc.date.accessioned	2021-04-13T03:35:46Z
dc.date.available	2021-04-13T03:35:46Z
dc.date.issued	2017-06
dc.description.abstract	Face recognition has been considered as a popular technique to recognise identity of a person. Many face recognition algorithms have been developed and modified by researchers. This paper will study the performance of three face recognition algorithms which are PCA, Modular PCA and LDA. These three face recognition algorithms will be implement to determine which algorithm has the best performance. The performance of these face recognition algorithms will be evaluated by 10-fold cross validation using ORL database. K-fold technique will divide the image database into k-fold that has the same size or segment. Nine-fold will be used for training sets and the remaining one-fold will be used as validation sets to calculate the accuracy of the system. PCA is known as eigenface projection to transfer the image space to low dimension feature space. Modular PCA is to divide an image into sub-image and then apply PCA on it. LDA is used to separate two or more class further and enclose population in the class. The recognition rate for PCA, Modular PCA and LDA is 96.25%, 85.75% and 89%, respectively	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/12772
dc.language.iso	en	en_US
dc.title	A comparison study on PCA, modular PCA and LDA for face recognition	en_US
dc.type	Other	en_US

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