An Individual-Difference-Aware Model for Cross-Person Gaze Estimation

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, VM is able to identify invalid samples, e.g. eye blinking images, and reduce their effects in modelling process. 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 dataset. We 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 are the winner of the GAZE 2021 EVE Challenge and our code can be found here https://github.com/bjj9/EVE_SCPT.