This paper presents a lightweight network for head pose estimation (HPE)
task. While previous approaches rely on convolutional neural networks, the
proposed network \textit{LwPosr} uses mixture of depthwise separable
convolutional (DSC) and transformer encoder layers which are structured in two
streams and three stages to provide fine-grained regression for predicting head
poses. The quantitative and qualitative demonstration is provided to show that
the proposed network is able to learn head poses efficiently while using less
parameter space. Extensive ablations are conducted using three open-source
datasets namely 300W-LP, AFLW2000, and BIWI datasets. To our knowledge, (1)
\textit{LwPosr} is the lightest network proposed for estimating head poses
compared to both keypoints-based and keypoints-free approaches; (2) it sets a
benchmark for both overperforming the previous lightweight network on mean
absolute error and on reducing number of parameters; (3) it is first of its
kind to use mixture of DSCs and transformer encoders for HPE. This approach is
suitable for mobile devices which require lightweight networks.

本文提出了一种基于深度可分离卷积和变换器编码器层结构的两个流和三个阶段的轻量级网络 LwPose，用于头部姿势估计 (HPE) 任务，与此前的方法相比，该方法具有更高的效率和更小的空间要求，适用于移动设备。