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