  Fast and accurate upper-body and head pose estimation is a key task for
automatic monitoring of driver attention, a challenging context characterized
by severe illumination changes, occlusions and extreme poses. In this work, we
present a new deep learning framework for head localization and pose estimation
on depth images. The core of the proposal is a regression neural network,
called POSEidon, which is composed of three independent convolutional nets
followed by a fusion layer, specially conceived for understanding the pose by
depth. In addition, to recover the intrinsic value of face appearance for
understanding head position and orientation, we propose a new Face-from-Depth
approach for learning image faces from depth. Results in face reconstruction
are qualitatively impressive. We test the proposed framework on two public
datasets, namely Biwi Kinect Head Pose and ICT-3DHP, and on Pandora, a new
challenging dataset mainly inspired by the automotive setup. Results show that
our method overcomes all recent state-of-art works, running in real time at
more than 30 frames per second.
