  Monocular head pose estimation requires learning a model that computes the
intrinsic Euler angles for pose (yaw, pitch, roll) from an input image of human
face. Annotating ground truth head pose angles for images in the wild is
difficult and requires ad-hoc fitting procedures (which provides only coarse
and approximate annotations). This highlights the need for approaches which can
train on data captured in controlled environment and generalize on the images
in the wild (with varying appearance and illumination of the face). Most
present day deep learning approaches which learn a regression function directly
on the input images fail to do so. To this end, we propose to use a higher
level representation to regress the head pose while using deep learning
architectures. More specifically, we use the uncertainty maps in the form of 2D
soft localization heatmap images over five facial keypoints, namely left ear,
right ear, left eye, right eye and nose, and pass them through an convolutional
neural network to regress the head-pose. We show head pose estimation results
on two challenging benchmarks BIWI and AFLW and our approach surpasses the
state of the art on both the datasets.
