Face Recognition & Principal Component Analysis Method: Algorithm, Simulation & Discussion

 
9783659461453: Face Recognition & Principal Component Analysis Method: Algorithm, Simulation & Discussion

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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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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Paul, Liton Chandra / Suman, Abdulla al
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Book Description Book Condition: New. Publisher/Verlag: LAP Lambert Academic Publishing | Algorithm, Simulation & Discussion | 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. | Format: Paperback | Language/Sprache: english | 80 pp. Bookseller Inventory # K9783659461453

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Liton Chandra Paul
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ISBN 10: 3659461458 ISBN 13: 9783659461453
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Book Description LAP Lambert Academic Publishing Okt 2013, 2013. Taschenbuch. Book Condition: Neu. 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. Bookseller Inventory # 9783659461453

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Liton Chandra Paul
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Book Description LAP Lambert Academic Publishing Okt 2013, 2013. Taschenbuch. Book Condition: Neu. 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. Bookseller Inventory # 9783659461453

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Liton Chandra Paul, Abdulla al Suman
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Book Description LAP Lambert Academic Publishing, 2013. Paperback. Book Condition: New. Language: English . Brand New Book. 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. Bookseller Inventory # KNV9783659461453

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