# Improving Head Pose Estimation with a Combined Loss and Bounding Box Margin Adjustment

arXiv: https://arxiv.org/abs/1905.08609 · HTML: https://mlsanity.com/paper/1905.08609/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2019.

## Built on by (most cited first, 8 of 8)

- [HHP-Net: A light Heteroscedastic neural network for Head Pose estimation with uncertainty](https://mlsanity.com/paper/2111.01440/) (2021)
- [DirectMHP: Direct 2D Multi-Person Head Pose Estimation with Full-range Angles](https://mlsanity.com/paper/2302.01110/) (2023)
- [LwPosr: Lightweight Efficient Fine-Grained Head Pose Estimation](https://mlsanity.com/paper/2202.03544/) (2022)
- [HeadPosr: End-to-end Trainable Head Pose Estimation using Transformer Encoders](https://mlsanity.com/paper/2202.03548/) (2022)
- [Boosting of Head Pose Estimation by Knowledge Distillation](https://mlsanity.com/paper/2108.09183/) (2021)
- [Location-guided Head Pose Estimation for Fisheye Image](https://mlsanity.com/paper/2402.18320/) (2024)
- [Cascaded Multi-Scale Attention for Enhanced Multi-Scale Feature Extraction and Interaction with Low-Resolution Images](https://mlsanity.com/paper/2412.02197/) (2024)
- [GADS: A Super Lightweight Model for Head Pose Estimation](https://mlsanity.com/paper/2504.15751/) (2025)

## Abstract (arXiv)

We address a problem of estimating pose of a person's head from its RGB image. The employment of CNNs for the problem has contributed to significant improvement in accuracy in recent works. However, we show that the following two methods, despite their simplicity, can attain further improvement: (i) proper adjustment of the margin of bounding box of a detected face, and (ii) choice of loss functions. We show that the integration of these two methods achieve the new state-of-the-art on standard benchmark datasets for in-the-wild head pose estimation.