  We propose a novel method on refining cross-person gaze prediction task with
eye/face images only by explicitly modelling the person-specific differences.
Specifically, we first assume that we can obtain some initial gaze prediction
results with existing method, which we refer to as InitNet, and then introduce
three modules, the Validity Module (VM), Self-Calibration (SC) and
Person-specific Transform (PT)) Module. By predicting the reliability of
current eye/face images, our VM is able to identify invalid samples, e.g. eye
blinking images, and reduce their effects in our modelling process. Our SC and
PT module then learn to compensate for the differences on valid samples only.
The former models the translation offsets by bridging the gap between initial
predictions and dataset-wise distribution. And the later learns more general
person-specific transformation by incorporating the information from existing
initial predictions of the same person. We validate our ideas on three publicly
available datasets, EVE, XGaze and MPIIGaze and demonstrate that our proposed
method outperforms the SOTA methods significantly on all of them, e.g.
respectively 21.7%, 36.0% and 32.9% relative performance improvements. We won
the GAZE 2021 Competition on the EVE dataset. Our code can be found here
https://github.com/bjj9/EVE_SCPT.
