# 2D Image head pose estimation via latent space regression under occlusion settings

arXiv: https://arxiv.org/abs/2311.06038 · HTML: https://mlsanity.com/paper/2311.06038/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 43 references that are in the map.
- First posted on arXiv 10 Nov 2023.

## Builds on (most cited first, 14 of 14)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Towards Fast, Accurate and Stable 3D Dense Face Alignment](https://mlsanity.com/paper/2009.09960/) (2020)
- [Fine-Grained Head Pose Estimation Without Keypoints](https://mlsanity.com/paper/1710.00925/) (2017)
- [Face Alignment in Full Pose Range: A 3D Total Solution](https://mlsanity.com/paper/1804.01005/) (2018)
- [6D Rotation Representation For Unconstrained Head Pose Estimation](https://mlsanity.com/paper/2202.12555/) (2022)
- [img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation](https://mlsanity.com/paper/2012.07791/) (2020)
- [Towards Large-Pose Face Frontalization in the Wild](https://mlsanity.com/paper/1704.06244/) (2017)
- [WHENet: Real-time Fine-Grained Estimation for Wide Range Head Pose](https://mlsanity.com/paper/2005.10353/) (2020)
- [Effective Face Frontalization in Unconstrained Images](https://mlsanity.com/paper/1411.7964/) (2014)
- [DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D Head Alignment from a Single Image](https://mlsanity.com/paper/2204.03688/) (2022)
- [POSEidon: Face-from-Depth for Driver Pose Estimation](https://mlsanity.com/paper/1611.10195/) (2016)
- [Robust Facial Landmark Detection under Significant Head Poses and Occlusion](https://mlsanity.com/paper/1709.08127/) (2017)
- [Simultaneous Facial Landmark Detection, Pose and Deformation Estimation under Facial Occlusion](https://mlsanity.com/paper/1709.08130/) (2017)
- [Single Image based Head Pose Estimation with Spherical Parameterization and 3D Morphing](https://mlsanity.com/paper/1907.09217/) (2019)

## Built on by (most cited first, 3 of 3)

- [Latent Embedding Clustering for Occlusion Robust Head Pose Estimation](https://mlsanity.com/paper/2403.20251/) (2024)
- [On the power of data augmentation for head pose estimation](https://mlsanity.com/paper/2407.05357/) (2024)
- [Bidirectional Regression for Monocular 6DoF Head Pose Estimation and Reference System Alignment](https://mlsanity.com/paper/2407.14136/) (2024)

## Abstract (arXiv)

Head orientation is a challenging Computer Vision problem that has been extensively researched having a wide variety of applications. However, current state-of-the-art systems still underperform in the presence of occlusions and are unreliable for many task applications in such scenarios. This work proposes a novel deep learning approach for the problem of head pose estimation under occlusions. The strategy is based on latent space regression as a fundamental key to better structure the problem for occluded scenarios. Our model surpasses several state-of-the-art methodologies for occluded HPE, and achieves similar accuracy for non-occluded scenarios. We demonstrate the usefulness of the proposed approach with: (i) two synthetically occluded versions of the BIWI and AFLW2000 datasets, (ii) real-life occlusions of the Pandora dataset, and (iii) a real-life application to human-robot interaction scenarios where face occlusions often occur. Specifically, the autonomous feeding from a robotic arm.