  We address the challenging problem of RGB image-based head pose estimation.
We first reformulate head pose representation learning to constrain it to a
bounded space. Head pose represented as vector projection or vector angles
shows helpful to improving performance. Further, a ranking loss combined with
MSE regression loss is proposed. The ranking loss supervises a neural network
with paired samples of the same person and penalises incorrect ordering of pose
prediction. Analysis on this new loss function suggests it contributes to a
better local feature extractor, where features are generalised to Abstract
Landmarks which are pose-related features instead of pose-irrelevant
information such as identity, age, and lighting. Extensive experiments show
that our method significantly outperforms the current state-of-the-art schemes
on public datasets: AFLW2000 and BIWI. Our model achieves significant
improvements over previous SOTA MAE on AFLW2000 and BIWI from 4.50 to 3.66 and
from 4.0 to 3.71 respectively. Source code will be made available at:
https://github.com/seathiefwang/RankHeadPose.
