  Head pose estimation, which computes the intrinsic Euler angles (yaw, pitch,
roll) from the human, is crucial for gaze estimation, face alignment, and 3D
reconstruction. Traditional approaches heavily relies on the accuracy of facial
landmarks. It limits their performances, especially when the visibility of the
face is not in good condition. In this paper, to do the estimation without
facial landmarks, we combine the coarse and fine regression output together for
a deep network. Utilizing more quantization units for the angles, a fine
classifier is trained with the help of other auxiliary coarse units.
Integrating regression is adopted to get the final prediction. The proposed
approach is evaluated on three challenging benchmarks. It achieves the
state-of-the-art on AFLW2000, BIWI and performs favorably on AFLW. The code has
been released on Github.
