Evaluation of Appearance-Based Methods and Implications for Gaze-Based Applications
This work addresses the gap in understanding performance of appearance-based gaze estimation for HCI applications, but it is incremental as it focuses on evaluation and tooling rather than novel method development.
The paper evaluated state-of-the-art appearance-based gaze estimation methods in various interaction scenarios, finding performance differences with and without personal calibration, indoors/outdoors, and for users with/without glasses, and introduced OpenGaze as the first software toolkit to democratize its use in HCI.
Appearance-based gaze estimation methods that only require an off-the-shelf camera have significantly improved but they are still not yet widely used in the human-computer interaction (HCI) community. This is partly because it remains unclear how they perform compared to model-based approaches as well as dominant, special-purpose eye tracking equipment. To address this limitation, we evaluate the performance of state-of-the-art appearance-based gaze estimation for interaction scenarios with and without personal calibration, indoors and outdoors, for different sensing distances, as well as for users with and without glasses. We discuss the obtained findings and their implications for the most important gaze-based applications, namely explicit eye input, attentive user interfaces, gaze-based user modelling, and passive eye monitoring. To democratise the use of appearance-based gaze estimation and interaction in HCI, we finally present OpenGaze (www.opengaze.org), the first software toolkit for appearance-based gaze estimation and interaction.