Histogram Equalization & PCA based Face Recognition on MATLAB Platform: MATLAB based Histogram tech. & Face detection using Principle Component Analysis with detailed flow of Analysis & coding

This books deals with the implementation of histogram equalization and histogram matching in a simple step by step approach using Probability Distributive Function (PDF) and Cumulative Distributive Function (CDF) in MATLAB with detailed coding steps using suitable examples. Next is implementation of Principle Component Analysis based face detection. From “Principle Analysis Technique” we calculate covariance matrix & then we can calculate eigenvectors & eigenvalues for this matrix. The eigenvector with the highest eigenvalue is the principle component of data set.Unknown face reorganization is our next concern. By following the data base obtained from earlier face reorganization system, we can easily classify any test image. For implementation of this part first eigenfaces of both original images & testing images are calculated & an average value of the distance between each is calculated. A threshold is calculated from a fixed procedure. If the distance is less than threshold then the tested image is considered as face or else it is discarded. All techniques explained as implemented in MATLAB & complete working code has been given in book to help a Knowledge Thirst driven student.