Rotation Axis Focused Attention Network (RAFA-Net) for Estimating Head Pose.

Head pose is a vital indicator of human attention and behavior. Therefore, automatic estimation of head pose from images is key to many applications. In this paper, we propose a novel approach for head pose estimation from a single RGB image. Many existing approaches often predict head poses by localizing facial landmarks and then solve 2D to 3D correspondence problem with a mean head model. Such approaches rely entirely on the landmark detection accuracy, an ad-hoc alignment step, and the extraneous head model. To address this drawback, we present an end-to-end deep network, which explores rotation axis (yaw, pitch and roll) focused innovative attention mechanism to capture the subtle changes in images. The mechanism uses attentional spatial pooling from a self-attention layer and learns the importance over fine-grained to coarse spatial structures and combine them to capture rich semantic information concerning a given rotation axis. The evaluation of our approach using three benchmark datasets is very competitive to state-of-the-arts, including with and without landmark-based methods. Code can be found at https://github.com/ArdhenduBehera/RAFA-Net .