Human Face Recognition with Support Vector Machines: Algorithms and Applications - Softcover

Parthiban, Latha

 
9783639366990: Human Face Recognition with Support Vector Machines: Algorithms and Applications
Softcover
ISBN 10: 3639366999 ISBN 13: 9783639366990
Verlag: VDM Verlag Dr. Müller, 2011

Inhaltsangabe

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.

Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.

Reseña del editor

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.

Biografía del autor

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.

„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.

Verlag
VDM Verlag Dr. Müller
Erscheinungsdatum
2011
Sprache
Englisch
ISBN 10 
3639366999
ISBN 13 
9783639366990
Einband
Tapa blanda
Anzahl der Seiten
64
Kontakt zum Hersteller
Nicht verfügbar
Verantwortliche Person
Nicht verfügbar