  Model-based eye tracking has been a dominant approach for eye gaze tracking
because of its ability to generalize to different subjects, without the need of
any training data and eye gaze annotations. Model-based eye tracking, however,
is susceptible to eye feature detection errors, in particular for eye tracking
in the wild. To address this issue, we propose a Bayesian framework for
model-based eye tracking. The proposed system consists of a cascade-Bayesian
Convolutional Neural Network (c-BCNN) to capture the probabilistic
relationships between eye appearance and its landmarks, and a geometric eye
model to estimate eye gaze from the eye landmarks. Given a testing eye image,
the Bayesian framework can generate, through Bayesian inference, the eye gaze
distribution without explicit landmark detection and model training, based on
which it not only estimates the most likely eye gaze but also its uncertainty.
Furthermore, with Bayesian inference instead of point-based inference, our
model can not only generalize better to different sub-jects, head poses, and
environments but also is robust to image noise and landmark detection errors.
Finally, with the estimated gaze uncertainty, we can construct a cascade
architecture that allows us to progressively improve gaze estimation accuracy.
Compared to state-of-the-art model-based and learning-based methods, the
proposed Bayesian framework demonstrates significant improvement in
generalization capability across several benchmark datasets and in accuracy and
robustness under challenging real-world conditions.
