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arXiv · 2401.14095

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

Mingtao Yue·Tomomi Sayuda·Miles Pennington·Yusuke Sugano
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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.

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Mingtao Yue, Tomomi Sayuda, Miles Pennington, Yusuke Sugano. 2024-09-03. Evaluating User Experience and Data Quality in Gamified Data Collection for Appearance-Based Gaze Estimation. https://doi.org/10.1080/10447318.2024.2399873

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