RealHePoNet: a robust single-stage ConvNet for head pose estimation in the wild
Human head pose estimation in images has applications in many fields such as human-computer interaction or video surveillance tasks. In this work, we address this problem, defined here as the estimation of both vertical (tilt/pitch) and horizontal (pan/yaw) angles, through the use of a single Convolutional Neural Network (ConvNet) model, trying to balance precision and inference speed in order to maximize its usability in real-world applications...
Our model is trained over the combination of two datasets: ‘Pointing’04’ (aiming at covering a wide range of poses) and ‘Annotated Facial Landmarks in the Wild’ (in order to improve