  Mobile eye-tracking systems have been available for about a decade now and
are becoming increasingly popular in different fields of application, including
marketing, sociology, usability studies and linguistics. While the
user-friendliness and ergonomics of the hardware are developing at a rapid
pace, the software for the analysis of mobile eye-tracking data in some points
still lacks robustness and functionality. With this paper, we investigate which
state-of-the-art computer vision algorithms may be used to automate the
post-analysis of mobile eye-tracking data. For the case study in this paper, we
focus on mobile eye-tracker recordings made during human-human face-to-face
interactions. We compared two recent publicly available frameworks (YOLOv2 and
OpenPose) to relate the gaze location generated by the eye-tracker to the head
and hands visible in the scene camera data. In this paper we will show that the
use of this single-pipeline framework provides robust results, which are both
more accurate and faster than previous work in the field. Moreover, our
approach does not rely on manual interventions during this process.
