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

Authors :
Yue, Mingtao
Sayuda, Tomomi
Pennington, Miles
Sugano, Yusuke
Yue, Mingtao
Sayuda, Tomomi
Pennington, Miles
Sugano, Yusuke
Publication Year :
2024

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.

Details

Database :
OAIster
Publication Type :
Electronic Resource
Accession number :
edsoai.on1438518749
Document Type :
Electronic Resource