UCL logo

UCL Discovery

UCL home » Library Services » Electronic resources » UCL Discovery

Full-body person recognition system

Nakajima, C; Pontil, M; Heisele, B; Poggio, T; (2003) Full-body person recognition system. PATTERN RECOGN , 36 (9) 1997 - 2006. 10.1016/S0031-3203(03)00061-X.

Full text not available from this repository.


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.

Type: Article
Title: Full-body person recognition system
DOI: 10.1016/S0031-3203(03)00061-X
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
URI: http://discovery.ucl.ac.uk/id/eprint/164133
Downloads since deposit
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item