%0 Journal Article
	%A Liton Jude Rozario and  Mohammad Reduanul Haque and  Md. Ziarul Islam and  Mohammad Shorif Uddin
	%D 2014
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 93, 2014
	%T Quantitative Analysis of PCA, ICA, LDA and SVM in Face Recognition
	%U https://publications.waset.org/pdf/9999412
	%V 93
	%X Face recognition is a technique to automatically
identify or verify individuals. It receives great attention in
identification, authentication, security and many more applications.
Diverse methods had been proposed for this purpose and also a lot of
comparative studies were performed. However, researchers could not
reach unified conclusion. In this paper, we are reporting an extensive
quantitative accuracy analysis of four most widely used face
recognition algorithms: Principal Component Analysis (PCA),
Independent Component Analysis (ICA), Linear Discriminant
Analysis (LDA) and Support Vector Machine (SVM) using AT&T,
Sheffield and Bangladeshi people face databases under diverse
situations such as illumination, alignment and pose variations.

	%P 1613 - 1616