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Face Recognition & Principal Component Analysis Method

Name: Face Recognition & Principal Component Analysis Method
Brand: LAP Lambert Academic Publishing
SKU: 02201775
Price: 35.74 EUR
Availability: InStock
Author: Liton Chandra Paul
ISBN: 9783659461453

Algorithm, Simulation & Discussion

Liton Chandra Paul

Abdulla al Suman

Language

English

Book Paperback

Libristo code: 02201775

Publishers LAP Lambert Academic Publishing, October 2013

This book mainly addresses the building of face recognition system and Principal Component Analysis... Full description

Libristo code: 02201775

86 b

35.74 €

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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.