Human face detection and tracking based on supervised learning - art. no. 67862A

Abstract

In this paper a novel method of human face detection and tracking based on supervised learning for video sequence is designed. The system is composed of a face detector using boosted rectangular filters with a new representative based integration method, a linear capture model and a quadric tracking model. The main contribution of this paper is a new view to face tracking solutions on condition that a robust real-time detector is adopted first. It differs fundamentally from traditional tracking algorithms for that it organically combines fast and robust detection with efficient capture and tracking which can be easily implemented in practical video systems while obtaining a satisfying real-time performance. Experimental results show that this algorithm can finely meet the reliability and effectiveness demands of video surveillance system.