  Appearance-based gaze estimation provides relatively unconstrained gaze
tracking. However, subject-independent models achieve limited accuracy partly
due to individual variations. To improve estimation, we propose a novel gaze
decomposition method and a single gaze point calibration method, motivated by
our finding that the inter-subject squared bias exceeds the intra-subject
variance for a subject-independent estimator. We decompose the gaze angle into
a subject-dependent bias term and a subject-independent term between the gaze
angle and the bias. The subject-independent term is estimated by a deep
convolutional network. For calibration-free tracking, we set the
subject-dependent bias term to zero. For single gaze point calibration, we
estimate the bias from a few images taken as the subject gazes at a point.
Experiments on three datasets indicate that as a calibration-free estimator,
the proposed method outperforms the state-of-the-art methods by up to $10.0\%$.
The proposed calibration method is robust and reduces estimation error
significantly (up to $35.6\%$), achieving state-of-the-art performance for
appearance-based eye trackers with calibration.
