Real time tracking and modeling of faces: an EKF-based analysis by synthesis approach

Abstract

A real-time system for tracking and modeling of faces using an analysis-by-synthesis approach is presented. A 3D face model is texture-mapped with a head-on view of the face. Feature points in the face-texture are then selected based on image Hessians. The selected points of the rendered image are tracked in the incoming video using normalized correlation. The result is fed into an extended Kalman filter to recover camera geometry, head pose, and structure from motion. This information is used to rigidly move the face model to render the next image needed for tracking. Every point is tracked from the Kalman filter's estimated position. The variance of each measurement is estimated using a number of factors, including the residual error and the angle between the surface normal and the camera. The estimated head pose can be used to warp the face in the incoming video back to frontal position, and parts of the image can then be subject to eigenspace coding for efficient transmission. The mouth texture is transmitted in this way using 50 bits per frame plus overhead from the person specific eigenspace. The face tracking system runs at 30 Hz, coding the mouth texture slows it down to 12 Hz.