  With an ever-increasing number of mobile devices competing for our attention,
quantifying when, how often, or for how long users visually attend to their
devices has emerged as a core challenge in mobile human-computer interaction.
Encouraged by recent advances in automatic eye contact detection using machine
learning and device-integrated cameras, we provide a fundamental investigation
into the feasibility of quantifying visual attention during everyday mobile
interactions. We identify core challenges and sources of errors associated with
sensing attention on mobile devices in the wild, including the impact of face
and eye visibility, the importance of robust head pose estimation, and the need
for accurate gaze estimation. Based on this analysis, we propose future
research directions and discuss how eye contact detection represents the
foundation for exciting new applications towards next-generation pervasive
attentive user interfaces.
