People Recognition in Image Sequences by Supervised Learning [report]

Chikahito Nakajima, Massimiliano Pontil, Bernd Heisele, Tomaso Poggio
2000 unpublished
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 classiers (SVMs). Di erent types of multiclass strategies based on SVMs are explored and compared to k-Nearest Neighbors classi ers (kNNs). The system works in real time and shows high performance rates for people recognition throughout one day. Public
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doi:10.21236/ada459706 fatcat:ripxmufpg5dvhppptp7kamik3u