By being able to predict multicue gaze for open signed video content, there can be coding gains without loss of perceived quality.
We have developed a face orientation tracker based upon grid-based likelihood ratio trackers, using profile and frontal face detections. These cues are combined using a grid-based Bayesian state estimation algorithm to form a probability surface for each frame. This gaze predictor outperforms a static gaze prediction and one based on face locations within the frame.
Computer Vision Group
Dept of Computer Science,
University of Bristol
For more information about our work or opportunities to join or visit the group, email vision at cs.bris.ac.uk