# Hybrid coarse-fine classification for head pose estimation

arXiv: https://arxiv.org/abs/1901.06778 · HTML: https://mlsanity.com/paper/1901.06778/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2019.

## Built on by (most cited first, 2 of 2)

- [How Self-Supervised Learning Can be Used for Fine-Grained Head Pose Estimation?](https://mlsanity.com/paper/2108.04893/) (2021)
- [Boosting of Head Pose Estimation by Knowledge Distillation](https://mlsanity.com/paper/2108.09183/) (2021)

## Abstract (arXiv)

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.