Evaluating User Experience and Data Quality in Gamified Data Collection for Appearance-Based Gaze Estimation
Abstract
Appearance-based gaze estimation, which uses only a regular camera to estimate human gaze, is important in various application fields. While the technique faces data bias issues, data collection protocol is often demanding, and collecting data from a wide range of participants is difficult. It is an important challenge to design opportunities that allow a diverse range of people to participate while ensuring the quality of the training data. To tackle this challenge, we introduce a novel gamified approach for collecting training data. In this game, two players communicate words via eye gaze through a transparent letter board. Images captured during gameplay serve as valuable training data for gaze estimation models. The game is designed as a physical installation that involves communication between players, and it is expected to attract the interest of diverse participants. We assess the game's significance on data quality and user experience through a comparative user study.
Keywords
Cite
@article{arxiv.2401.14095,
title = {Evaluating User Experience and Data Quality in Gamified Data Collection for Appearance-Based Gaze Estimation},
author = {Mingtao Yue and Tomomi Sayuda and Miles Pennington and Yusuke Sugano},
journal= {arXiv preprint arXiv:2401.14095},
year = {2024}
}
Comments
Accepted to International Journal of Human-Computer Interaction (IJHCI)