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Book synopsis

This book mainly addresses the building of face recognition system and Principal Component Analysis (PCA) method in details. PCA is a statistical approach used for reducing the number of variables in face recognition. In PCA, every image in the training set is represented as a linear combination of weighted eigenvectors called eigenfaces. These eigenvectors are obtained from covariance matrix of a training image set called as basis function. The weights are found out after selecting a set of most relevant Eigenfaces. Recognition is performed by projecting a test image onto the subspace spanned by the eigenfaces and then classification is done by measuring Euclidean distance. A number of experiments were done to evaluate the performance of the face recognition system. Here, I used a training database of students of ETE-07 series, RUET, Rajshahi-6204, Bangladesh.

Book details

• Full title: Face Recognition & Principal Component Analysis Method
• Subtitle: Algorithm, Simulation & Discussion
• Author: Liton Chandra Paul, Abdulla al Suman
• Language: English
• Binding: Paperback
• Number of pages: 80
• EAN: 9783659461453
• ID: 02201775
• Publisher: LAP Lambert Academic Publishing
• Weight: 137 g
• Dimensions: 220 × 150 × 5 mm
• Date of publishing: 22. October 2013

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