PCA based Eigen Face Generation
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 seminar class Active In SP Posts: 5,361 Joined: Feb 2011 03-03-2011, 10:51 AM   minippt.ppt (Size: 1.27 MB / Downloads: 59) Principal Component Analysis • PCA is a powerful tool for analyzing data. • It is a way of identifying patterns in data, and expressing the data in such a way as to highlight their similarities and differences. • Principal component analysis (PCA) involves a mathematical procedure that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components. • Principal Components Analysis is a method that reduces data dimensionality by performing a covariance analysis between factors. • One of the main applications of the PCA in Computer Vision is in facial recognition. • By means of PCA one can transform each original image of the training set into a corresponding Eigen face Eigen Face Generation • Each Eigen face represents only certain features of the face, which may or may not be present in the original image. • If the feature is present in the original image to a higher degree, the share of the corresponding Eigen face in the” sum” of the Eigen faces should be greater. • First, the original images of the training set are transformed in to a set of Eigen faces E. • An eigenvector of a matrix is a vector such that, if multiplied with the matrix, the result is always an integer multiple of that vector. This integer value is the corresponding Eigen value of the eigenvector • Eigenvectors possess following properties: • They can be determined only for square matrices • There are n eigenvectors (and corresponding Eigen values) in a n × n matrix. • All eigenvectors are perpendicular, i.e. at right angle with each other. Steps Required for PCA Algorithm Requirements Operating System • Linux (32-bit) • Mac OS X (Intel 32-bit) • Mac OS X (Intel 64-bit) • Windows (32-bit) • Windows (64-bit) Hardware Requirements: • CPU 32 bit intel core duo Pentium IV processor with 1.86GHz or similar • 512 MB RAM • Disk Space 510 MB Expected Output: Eigen faces of the given training