Full-body person recognition system.
1997 - 2006.
We describe a system that learns from examples to recognize persons in images taken indoors. Images of full-body persons are represented by color-based and shape-based features. Recognition is carried out through combinations of Support Vector Machine (SVM) classifiers. Different types of multi-class strategies based on SVMs are explored and compared to k-Nearest Neighbors classifiers. The experimental results show high recognition rates and indicate the strength of SVM-based classifiers to improve both generalization and run-time performance. The system works in real-time. (C) 2003 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
|Title:||Full-body person recognition system|
|Keywords:||multi-class classification, person recognition, pattern classification, support vector machines, surveillance systems, object recognition, SUPPORT VECTOR MACHINES, FACE RECOGNITION, OBJECT RECOGNITION, CLASSIFICATION, NETWORKS|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science
UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
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