  Onfocus detection aims at identifying whether the focus of the individual
captured by a camera is on the camera or not. Based on the behavioral research,
the focus of an individual during face-to-camera communication leads to a
special type of eye contact, i.e., the individual-camera eye contact, which is
a powerful signal in social communication and plays a crucial role in
recognizing irregular individual status (e.g., lying or suffering mental
disease) and special purposes (e.g., seeking help or attracting fans). Thus,
developing effective onfocus detection algorithms is of significance for
assisting the criminal investigation, disease discovery, and social behavior
analysis. However, the review of the literature shows that very few efforts
have been made toward the development of onfocus detector due to the lack of
large-scale public available datasets as well as the challenging nature of this
task. To this end, this paper engages in the onfocus detection research by
addressing the above two issues. Firstly, we build a large-scale onfocus
detection dataset, named as the OnFocus Detection In the Wild (OFDIW). It
consists of 20,623 images in unconstrained capture conditions (thus called ``in
the wild'') and contains individuals with diverse emotions, ages, facial
characteristics, and rich interactions with surrounding objects and background
scenes. On top of that, we propose a novel end-to-end deep model, i.e., the
eye-context interaction inferring network (ECIIN), for onfocus detection, which
explores eye-context interaction via dynamic capsule routing. Finally,
comprehensive experiments are conducted on the proposed OFDIW dataset to
benchmark the existing learning models and demonstrate the effectiveness of the
proposed ECIIN. The project (containing both datasets and codes) is at
https://github.com/wintercho/focus.
