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Quantification of human attention is key to several tasks in mobilehuman-computer interaction (HCI), such as predicting user interruptibility,estimating noticeability of user interface content, or measuring userengagement. Previous works to study mobile attentive behaviour requiredspecial-purpose eye tracking equipment or constrained users' mobility. Wepropose a novel method to sense and analyse visual attention on mobile devicesduring everyday interactions. We demonstrate the capabilities of our method onthe sample task of eye contact detection that has recently attracted increasingresearch interest in mobile HCI. Our method builds on a state-of-the-art methodfor unsupervised eye contact detection and extends it to address challengesspecific to mobile interactive scenarios. Through evaluation on two currentdatasets, we demonstrate significant performance improvements for eye contactdetection across mobile devices, users, or environmental conditions. Moreover,we discuss how our method enables the calculation of additional attentionmetrics that, for the first time, enable researchers from different domains tostudy and quantify attention allocation during mobile interactions in the wild.

Accurate and Robust Eye Contact Detection During Everyday Mobile Device Interactions