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Head pose is an important indicator of a person's attention, gestures, and communicative behavior with applications in human computer interaction, multimedia and vision systems. In this paper, we present a novel head pose estimation system by performing head region detection using the Kinect , followed by face detection, feature tracking, and finally head pose estimation using an active camera. Ten feature points on the face are defined and tracked by an Active Appearance Model (AAM). We propose to use the scene flow approach to estimate the head pose from 2D video sequences. This estimation is based upon a generic 3D head model through the prior knowledge of the head shape and the geometric relationship between the 2D images and a 3D generic model. We have tested our head pose estimation algorithm with various cameras at various distances in real time. The experiments demonstrate the feasibility and advantages of our system.
Date of Conference: 9-13 July 2012