Towards Robust and Unconstrained Full Range of Rotation Head Pose
  Estimation
Thorsten Hempel Ahmed A. Abdelrahman Ayoub Al-Hamadi
Estimating the head pose of a person is a crucial problem for numerous
applications that is yet mainly addressed as a subtask of frontal pose
prediction. We present a novel method for unconstrained end-to-end head pose
estimation to tackle the challenging task of full range of orientation head
pose prediction. We address the issue of ambiguous rotation labels by
introducing the rotation matrix formalism for our ground truth data and propose
a continuous 6D rotation matrix representation for efficient and robust direct
regression. This allows to efficiently learn full rotation appearance and to
overcome the limitations of the current state-of-the-art. Together with new
accumulated training data that provides full head pose rotation data and a
geodesic loss approach for stable learning, we design an advanced model that is
able to predict an extended range of head orientations. An extensive evaluation
on public datasets demonstrates that our method significantly outperforms other
state-of-the-art methods in an efficient and robust manner, while its advanced
prediction range allows the expansion of the application area. We open-source
our training and testing code along with our trained models:
https://github.com/thohemp/6DRepNet360.