Analyst classification of domain detections by a standard severity taxonomy for up 10,000 domain detections. (PP-M-MDFC-S-A-101)
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.
This book is the consequence of a NATO ASI held at the Chateau de BONAS, from June 23 to July 4, 1980. It contains the tutorial lectures and some papers presented at the Institute. The book is divided in four sections: Issue.s-, of general interest. Some topics are broader than the proper techniques of image processing, such as complexity, clustering, topology, physiology; but they may be of interest ... Feature detect'ion and evaluation. The first level feature detections are examined: edges and textures. Reorganization and improvement of the results are obtained by relaxation and opti mization process. Cooperative process are examined. Scenes and shapes. concerns higher level problems, and representation of images such as map and line-drawings. Applications in remote sensing, scene analysis, of one or of a sequence of images. It is hoped that this book will serve to update a domain In fast evolution. Acknowledgment: This ASI, and this book, have been made possible by the financial support of the NATO Scientific Affairs Division, and the material support of INRIA and the Institut de Programmation of the Universite P. et M. Curie. vii J. C. Simon and R. M. Haralick (eds.), Digital Image Processing, vii. Copyright © 1981 by D. Reidel Publishing Company. APPLICATION OF COMPLEXITY OF COMPUTATIONS TO SIGNAL PROCESSING S. Winograd IBM Thomas J. Watson Research Center Yorktown Heights, New York, U.S.A.