English

Evaluating User Experience and Data Quality in Gamified Data Collection for Appearance-Based Gaze Estimation

Human-Computer Interaction 2024-09-04 v2

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)

R2 v1 2026-06-28T14:26:55.960Z