Evaluating Appearance-Based Gaze Pattern for Human-Robot Interaction
Linlin Cheng, Koen V. Hindriks, Mark De Bruijn, Artem V. Belopolsky · 2025
Appearance-based gaze estimation, an accessible and unobtrusive alternative to eye tracking, has advanced significantly, yet their adoption in human-robot interaction (HRI) remains limited. A key barrier is the lack of clarity on how they compare to high-precision eye trackers. To address this, we evaluate this method against eye-tracker glasses in an HRI setting using calibration and attention detection tasks. We assess performance across different cameras (4K and robot’s built-in camera) and participant conditions (with and without glasses). Results show that the 4K camera and participants without glasses yield higher accuracy and precision. With a simple offset correction, this method achieves comparable performance to eye-tracker glasses for average gaze pattern but struggles with detecting gaze patterns over time. It also demonstrates potential for real-time robot attention detection. We conclude that appearance-based gaze estimation is a viable, cost-effective alternative to traditional eye tracking in HRI, particularly for average gaze pattern detection.