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Face Recognition & Principal Component Analysis Method: Algorithm, Simulation & Discussion - Softcover
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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About the Author:
Liton Chandra Paul (Nominated for President Gold Medal,1st Class 1st With Honors) received B.Sc in ETE from RUET, Rajshahi, Bangladesh.Currently, he is working as a lecturer of ETE department at PUST, Pabna, Bangladesh.He has more than 8 international journals & conference papers.He has also another book published by LAP LAMBERT Academic Publishing
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- PublisherLAP LAMBERT Academic Publishing
- Publication date2013
- ISBN 10 3659461458
- ISBN 13 9783659461453
- BindingPaperback
- Number of pages80
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Face Recognition & Principal Component Analysis Method
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ISBN 10: 3659461458
ISBN 13: 9783659461453
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Book Description Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 80 pp. Englisch. Seller Inventory # 9783659461453
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Face Recognition & Principal Component Analysis Method: Algorithm, Simulation & Discussion
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(2013)
ISBN 10: 3659461458
ISBN 13: 9783659461453
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Book Description Paperback. Condition: Brand New. 80 pages. 8.66x5.91x0.19 inches. In Stock. Seller Inventory # zk3659461458
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Face Recognition & Principal Component Analysis Method : Algorithm, Simulation & Discussion
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LAP LAMBERT Academic Publishing
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ISBN 10: 3659461458
ISBN 13: 9783659461453
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Book Description Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Seller Inventory # 9783659461453
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Face Recognition & Principal Component Analysis Method
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Book Description Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Paul Liton ChandraLiton Chandra Paul (Nominated for President Gold Medal,1st Class 1st With Honors) received B.Sc in ETE from RUET, Rajshahi, Bangladesh.Currently, he is working as a lecturer of ETE department at PUST, Pabna, Banglad. Seller Inventory # 5157518
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