People Recognition in Image Sequences by Supervised Learning
Chikahito Nakajima, Massimiliano Pontil, Bernd Heisele, Tomaso Poggio · 2000
This publication can be retrieved by anonymous ftp to publications.ai.mit.edu. The pathname for this publication is: ai-publications/1500-1999/AIM-1688.ps.Z We describe a system that learns from examples to recognize people in images taken indoors. Images of people are represented by color-based and shape-based features. Recognition is carried out through combinations of Support Vector Machine classi-ers (SVMs). Dierent types of multiclass strategies based on SVMs are explored and compared to k-Nearest Neighbors classiers (kNNs). The system works in real time and shows high performance rates for people recognition throughout one day. Copyright c