  Human head pose estimation is an essential problem in facial analysis in
recent years that has a lot of computer vision applications such as gaze
estimation, virtual reality, and driver assistance. Because of the importance
of the head pose estimation problem, it is necessary to design a compact model
to resolve this task in order to reduce the computational cost when deploying
on facial analysis-based applications such as large camera surveillance
systems, AI cameras while maintaining accuracy. In this work, we propose a
lightweight model that effectively addresses the head pose estimation problem.
Our approach has two main steps. 1) We first train many teacher models on the
synthesis dataset - 300W-LPA to get the head pose pseudo labels. 2) We design
an architecture with the ResNet18 backbone and train our proposed model with
the ensemble of these pseudo labels via the knowledge distillation process. To
evaluate the effectiveness of our model, we use AFLW-2000 and BIWI - two
real-world head pose datasets. Experimental results show that our proposed
model significantly improves the accuracy in comparison with the
state-of-the-art head pose estimation methods. Furthermore, our model has the
real-time speed of $\sim$300 FPS when inferring on Tesla V100.
