An Incremental Learning Method for Unconstrained Gaze Estimation

This paper presents an online learning algorithm for appearance-based gaze estimation that allows free head movement in a casual desktop environment. Our method avoids the lengthy calibration stage using an incremental learning approach. Our system keeps running as a background process on the desktop PC and continuously updates the estimation parameters by taking user’s operations on the PC monitor as input. To handle free head movement of a user, we propose a pose-based clustering approach that efficiently extends an appearance manifold model to handle the large variations of the head pose.

Publications

  • Yusuke Sugano, Yasuyuki Matsushita, Yoichi Sato and Hideki Koike, “An Incremental Learning Method for Unconstrained Gaze Estimation”, in Proc. European Conference on Computer Vision (ECCV2008), October 2008.