In the past decade, face recognition has received much attention by both the commercial and public sectors in biometrics. This book describes a highly accurate appearance-based approach for face recognition - Gabor-Boosting.The strong performance of the Gabor-Boosting face recognition is highlighted by combining three key leading edge techniques - Gabor wavelet transform, AdaBoost, Support Vector Machine (SVM). The Gabor wavelet transform is used to extract features which describe texture variations of human faces. The AdaBoost algorithm is used to select most significant features which represent different individuals. The SVM constructs a classifier with high recognition accuracy. The Gabor-Boosting face recognition is extended into multi-class classification domain. The results show that the performance is improved by applying loosely controlled face recognition in the multi-class classification. The Gabor-Boosting face recognition is robust under conditions of small number of examples and selection-bias. It gives no false detections for impostors and high acceptance rate for clients in face verification.
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1. Gabor-Boosting Face Recognition
Published by
VDM Verlag Nov 2009
(2009)
ISBN 10: 3639214609
ISBN 13: 9783639214604
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Book Description VDM Verlag Nov 2009, 2009. Taschenbuch. Condition: Neu. Neuware - In the past decade, face recognition has received much attention by both the commercial and public sectors in biometrics. This book describes a highly accurate appearance-based approach for face recognition - Gabor-Boosting.The strong performance of the Gabor-Boosting face recognition is highlighted by combining three key leading edge techniques - Gabor wavelet transform, AdaBoost, Support Vector Machine (SVM). The Gabor wavelet transform is used to extract features which describe texture variations of human faces. The AdaBoost algorithm is used to select most significant features which represent different individuals. The SVM constructs a classifier with high recognition accuracy. The Gabor-Boosting face recognition is extended into multi-class classification domain. The results show that the performance is improved by applying loosely controlled face recognition in the multi-class classification. The Gabor-Boosting face recognition is robust under conditions of small number of examples and selection-bias. It gives no false detections for impostors and high acceptance rate for clients in face verification. 256 pp. Englisch. Seller Inventory # 9783639214604
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2. Gabor-Boosting Face Recognition
Published by
VDM Verlag Nov 2009
(2009)
ISBN 10: 3639214609
ISBN 13: 9783639214604
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Taschenbuch
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Book Description VDM Verlag Nov 2009, 2009. Taschenbuch. Condition: Neu. Neuware - In the past decade, face recognition has received much attention by both the commercial and public sectors in biometrics. This book describes a highly accurate appearance-based approach for face recognition - Gabor-Boosting.The strong performance of the Gabor-Boosting face recognition is highlighted by combining three key leading edge techniques - Gabor wavelet transform, AdaBoost, Support Vector Machine (SVM). The Gabor wavelet transform is used to extract features which describe texture variations of human faces. The AdaBoost algorithm is used to select most significant features which represent different individuals. The SVM constructs a classifier with high recognition accuracy. The Gabor-Boosting face recognition is extended into multi-class classification domain. The results show that the performance is improved by applying loosely controlled face recognition in the multi-class classification. The Gabor-Boosting face recognition is robust under conditions of small number of examples and selection-bias. It gives no false detections for impostors and high acceptance rate for clients in face verification. 256 pp. Englisch. Seller Inventory # 9783639214604
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3. Gabor-Boosting Face Recognition : From Machine Learning Perspective
Published by
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(2009)
ISBN 10: 3639214609
ISBN 13: 9783639214604
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Book Description VDM Verlag Nov 2009, 2009. Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Neuware - In the past decade, face recognition has received much attention by both the commercial and public sectors in biometrics. This book describes a highly accurate appearance-based approach for face recognition - Gabor-Boosting.The strong performance of the Gabor-Boosting face recognition is highlighted by combining three key leading edge techniques - Gabor wavelet transform, AdaBoost, Support Vector Machine (SVM). The Gabor wavelet transform is used to extract features which describe texture variations of human faces. The AdaBoost algorithm is used to select most significant features which represent different individuals. The SVM constructs a classifier with high recognition accuracy. The Gabor-Boosting face recognition is extended into multi-class classification domain. The results show that the performance is improved by applying loosely controlled face recognition in the multi-class classification. The Gabor-Boosting face recognition is robust under conditions of small number of examples and selection-bias. It gives no false detections for impostors and high acceptance rate for clients in face verification. 256 pp. Englisch. Seller Inventory # 9783639214604
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4. Gabor-Boosting Face Recognition: From Machine Learning Perspective (Paperback)
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(2009)
ISBN 10: 3639214609
ISBN 13: 9783639214604
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Book Description VDM Verlag, Germany, 2009. Paperback. Condition: New. Language: English. Brand new Book. In the past decade, face recognition has received much attention by both the commercial and public sectors in biometrics. This book describes a highly accurate appearance-based approach for face recognition - Gabor-Boosting.The strong performance of the Gabor-Boosting face recognition is highlighted by combining three key leading edge techniques - Gabor wavelet transform, AdaBoost, Support Vector Machine (SVM). The Gabor wavelet transform is used to extract features which describe texture variations of human faces. The AdaBoost algorithm is used to select most significant features which represent different individuals. The SVM constructs a classifier with high recognition accuracy. The Gabor-Boosting face recognition is extended into multi-class classification domain. The results show that the performance is improved by applying loosely controlled face recognition in the multi-class classification. The Gabor-Boosting face recognition is robust under conditions of small number of examples and selection-bias. It gives no false detections for impostors and high acceptance rate for clients in face verification. Seller Inventory # KNV9783639214604
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