  Head pose estimation is a crucial problem for many tasks, such as driver
attention, fatigue detection, and human behaviour analysis. It is well known
that neural networks are better at handling classification problems than
regression problems. It is an extremely nonlinear process to let the network
output the angle value directly for optimization learning, and the weight
constraint of the loss function will be relatively weak. This paper proposes a
novel Bernoulli heatmap for head pose estimation from a single RGB image. Our
method can achieve the positioning of the head area while estimating the angles
of the head. The Bernoulli heatmap makes it possible to construct fully
convolutional neural networks without fully connected layers and provides a new
idea for the output form of head pose estimation. A deep convolutional neural
network (CNN) structure with multiscale representations is adopted to maintain
high-resolution information and low-resolution information in parallel. This
kind of structure can maintain rich, high-resolution representations. In
addition, channelwise fusion is adopted to make the fusion weights learnable
instead of simple addition with equal weights. As a result, the estimation is
spatially more precise and potentially more accurate. The effectiveness of the
proposed method is empirically demonstrated by comparing it with other
state-of-the-art methods on public datasets.
