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D. S. Pankaj and M. Wilscy, “Comparison of PCA, LDA and Gabor Features for Face Recognition Using Fuzzy Neural Network,” in Advances in Computing and Information Technology: Springer, 2013, pp. 413-422.

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• TITLE: An Overview of Principal Component Analysis

  AUTHORS: Sasan Karamizadeh, Shahidan M. Abdullah, Azizah A. Manaf, Mazdak Zamani, Alireza Hooman

  KEYWORDS: Biometric; PCA; Eigenvector; Covariance; Standard Deviation

  JOURNAL NAME: Journal of Signal and Information Processing, Vol.4 No.3B, October 17, 2013

  ABSTRACT: The principal component analysis (PCA) is a kind of algorithms in biometrics. It is a statistics technical and used orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables. PCA also is a tool to reduce multidimensional data to lower dimensions while retaining most of the information. It covers standard deviation, covariance, and eigenvectors. This background knowledge is meant to make the PCA section very straightforward, but can be skipped if the concepts are already familiar.