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Human Face Recognition with Support Vector Machines: Algorithms and Applications - Couverture souple
Synopsis
The computer vision problem of face recognition has over the years become a common high-requirement benchmark for machine learning methods. In the last decade, highly efficient face recognition systems have been developed that extensively use the nature of the image domain to achieve accurate real-time performance. The effectiveness of such systems are possible only with the progress of machine learning algorithms. Support vector machine learning is a relatively recent method that offers a good generalization performance in classification problems like face recognition. An algorithm based on Gabor texture information with SVM classifier is demonstrated in this book.The estimated model parameters serve as texture representation and experiments were performed on Yale,ORL and FERET databases to validate the feasibility of the method. The results showed that both Gabor magnitude and Gabor phase based texture representation technique with SVM classifier significantly outperformed the widely used Gabor energy based systems and other existing subspace methods. In addition, the feature level fusion of these two kinds of texture representations performs better than when used individually.
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Présentation de l'éditeur
The computer vision problem of face recognition has over the years become a common high-requirement benchmark for machine learning methods. In the last decade, highly efficient face recognition systems have been developed that extensively use the nature of the image domain to achieve accurate real-time performance. The effectiveness of such systems are possible only with the progress of machine learning algorithms. Support vector machine learning is a relatively recent method that offers a good generalization performance in classification problems like face recognition. An algorithm based on Gabor texture information with SVM classifier is demonstrated in this book.The estimated model parameters serve as texture representation and experiments were performed on Yale,ORL and FERET databases to validate the feasibility of the method. The results showed that both Gabor magnitude and Gabor phase based texture representation technique with SVM classifier significantly outperformed the widely used Gabor energy based systems and other existing subspace methods. In addition, the feature level fusion of these two kinds of texture representations performs better than when used individually.
Biographie de l'auteur
Latha Parthiban is working as Professor in Department of Computer Science and Engineering at SSN College of Engineering,India. She earned her B.E from Madras University, M.E from Anna University and PhD from Pondicherry Central University. Her current research area involves applications of data mining, machine learning and medical image processing.
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Human Face Recognition with Support Vector Machines
Edité par
VDM Verlag Dr. Müller, 2011
ISBN 10 : 3639366999
ISBN 13 : 9783639366990
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Kartoniert / Broschiert. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Parthiban LathaLatha Parthiban is working as Professor in Department of Computer Science and Engineering at SSN College of Engineering,India. She earned her B.E from Madras University, M.E from Anna University and PhD from Pondicherr. N° de réf. du vendeur 4981385
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Human Face Recognition with Support Vector Machines: Algorithms and Applications
Edité par
VDM Verlag Dr. Müller, 2011
ISBN 10 : 3639366999
ISBN 13 : 9783639366990
Neuf
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Human Face Recognition with Support Vector Machines Algorithms and Applications
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Human Face Recognition with Support Vector Machines
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Human Face Recognition with Support Vector Machines: Algorithms and Applications
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VDM Verlag Dr. Müller, 2011
ISBN 10 : 3639366999
ISBN 13 : 9783639366990
Neuf
Couverture souple
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The computer vision problem of face recognition has over the years become a common high-requirement benchmark for machine learning methods. In the last decade, highly efficient face recognition systems have been developed that extensively use the nature of the image domain to achieve accurate real-time performance. The effectiveness of such systems are possible only with the progress of machine learning algorithms. Support vector machine learning is a relatively recent method that offers a good generalization performance in classification problems like face recognition. An algorithm based on Gabor texture information with SVM classifier is demonstrated in this book.The estimated model parameters serve as texture representation and experiments were performed on Yale,ORL and FERET databases to validate the feasibility of the method. The results showed that both Gabor magnitude and Gabor phase based texture representation technique with SVM classifier significantly outperformed the widely used Gabor energy based systems and other existing subspace methods. In addition, the feature level fusion of these two kinds of texture representations performs better than when used individually. N° de réf. du vendeur 9783639366990
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Human Face Recognition with Support Vector Machines: Algorithms and Applications
Edité par
VDM Verlag Dr. Müller, 2011
ISBN 10 : 3639366999
ISBN 13 : 9783639366990
Neuf
Couverture souple
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