  In this paper we introduce a novel method to estimate the head pose of people
in single images starting from a small set of head keypoints. To this purpose,
we propose a regression model that exploits keypoints computed automatically by
2D pose estimation algorithms and outputs the head pose represented by yaw,
pitch, and roll. Our model is simple to implement and more efficient with
respect to the state of the art -- faster in inference and smaller in terms of
memory occupancy -- with comparable accuracy. Our method also provides a
measure of the heteroscedastic uncertainties associated with the three angles,
through an appropriately designed loss function; we show there is a correlation
between error and uncertainty values, thus this extra source of information may
be used in subsequent computational steps. As an example application, we
address social interaction analysis in images: we propose an algorithm for a
quantitative estimation of the level of interaction between people, starting
from their head poses and reasoning on their mutual positions. The code is
available at https://github.com/cantarinigiorgio/HHP-Net.
