Accurate and Robust Eye Contact Detection During Everyday Mobile Device Interactions
This addresses the need for quantifying human attention in mobile HCI without specialized equipment, enabling applications like predicting interruptibility and measuring engagement, though it is incremental as it builds on existing methods.
The paper tackles the problem of detecting eye contact during everyday mobile device interactions, proposing a method that improves performance across devices, users, and conditions, as demonstrated on two datasets with significant gains.
Quantification of human attention is key to several tasks in mobile human-computer interaction (HCI), such as predicting user interruptibility, estimating noticeability of user interface content, or measuring user engagement. Previous works to study mobile attentive behaviour required special-purpose eye tracking equipment or constrained users' mobility. We propose a novel method to sense and analyse visual attention on mobile devices during everyday interactions. We demonstrate the capabilities of our method on the sample task of eye contact detection that has recently attracted increasing research interest in mobile HCI. Our method builds on a state-of-the-art method for unsupervised eye contact detection and extends it to address challenges specific to mobile interactive scenarios. Through evaluation on two current datasets, we demonstrate significant performance improvements for eye contact detection across mobile devices, users, or environmental conditions. Moreover, we discuss how our method enables the calculation of additional attention metrics that, for the first time, enable researchers from different domains to study and quantify attention allocation during mobile interactions in the wild.