  The correct estimation of the head pose is a problem of the great importance
for many applications. For instance, it is an enabling technology in automotive
for driver attention monitoring. In this paper, we tackle the pose estimation
problem through a deep learning network working in regression manner.
Traditional methods usually rely on visual facial features, such as facial
landmarks or nose tip position. In contrast, we exploit a Convolutional Neural
Network (CNN) to perform head pose estimation directly from depth data. We
exploit a Siamese architecture and we propose a novel loss function to improve
the learning of the regression network layer. The system has been tested on two
public datasets, Biwi Kinect Head Pose and ICT-3DHP database. The reported
results demonstrate the improvement in accuracy with respect to current
state-of-the-art approaches and the real time capabilities of the overall
framework.
