Robust Eye Contact Detection in Natural Multi-Person Interactions Using Gaze and Speaking Behaviour

Philipp Müller, Michael Xuelin Huang, Xucong Zhang, Andreas Bulling

Proc. ACM International Symposium on Eye Tracking Research and Applications (ETRA), pp. 31:1-31:10, 2018.

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Abstract

Eye contact is one of the most important non-verbal social cues and fundamental to human interactions. However, detecting eye contact without specialized eye tracking equipment poses significant challenges, particularly for multiple people in real-world settings. We present a novel method to robustly detect eye contact in natural three- and four-person interactions using off-the-shelf ambient cameras. Our method exploits that, during conversations, people tend to look at the person who is currently speaking. Harnessing the correlation between people’s gaze and speaking behaviour therefore allows our method to automatically acquire training data during deployment and adaptively train eye contact detectors for each target user. We empirically evaluate the performance of our method on a recent dataset of natural group interactions and demonstrate that it achieves a relative improvement over the state-of-the-art method of more than 60%, and also improves over a head pose based baseline.

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