Face Recognition & Principal Component Analysis Method
Algorithm, Simulation & Discussion
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Product details:
- Publisher LAP Lambert Academic Publishing
- Date of Publication 1 January 2013
- ISBN 9783659461453
- Binding Paperback
- No. of pages80 pages
- Size 220x150 mm
- Language English 0
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Long description:
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.
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