# A Vector-based Representation to Enhance Head Pose Estimation

arXiv: https://arxiv.org/abs/2010.07184 · HTML: https://mlsanity.com/paper/2010.07184/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2020.

## Built on by (most cited first, 30 of 35)

- [6D Rotation Representation For Unconstrained Head Pose Estimation](https://mlsanity.com/paper/2202.12555/) (2022)
- [Visual Recognition with Deep Nearest Centroids](https://mlsanity.com/paper/2209.07383/) (2022)
- [FaceXFormer: A Unified Transformer for Facial Analysis](https://mlsanity.com/paper/2403.12960/) (2024)
- [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)
- [Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry](https://mlsanity.com/paper/2110.09772/) (2021)
- [Towards Robust and Unconstrained Full Range of Rotation Head Pose Estimation](https://mlsanity.com/paper/2309.07654/) (2023)
- [On the representation and methodology for wide and short range head pose estimation](https://mlsanity.com/paper/2401.05807/) (2024)
- [HHP-Net: A light Heteroscedastic neural network for Head Pose estimation with uncertainty](https://mlsanity.com/paper/2111.01440/) (2021)
- [Towards 3D Face Reconstruction in Perspective Projection: Estimating 6DoF Face Pose from Monocular Image](https://mlsanity.com/paper/2205.04126/) (2022)
- [CML-MOTS: Collaborative Multi-task Learning for Multi-Object Tracking and Segmentation](https://mlsanity.com/paper/2311.00987/) (2023)
- [Towards Unbiased Label Distribution Learning for Facial Pose Estimation Using Anisotropic Spherical Gaussian](https://mlsanity.com/paper/2208.09122/) (2022)
- [DirectMHP: Direct 2D Multi-Person Head Pose Estimation with Full-range Angles](https://mlsanity.com/paper/2302.01110/) (2023)
- [DSFNet: Dual Space Fusion Network for Occlusion-Robust 3D Dense Face Alignment](https://mlsanity.com/paper/2305.11522/) (2023)
- [Task-adaptive Q-Face](https://mlsanity.com/paper/2405.09059/) (2024)
- [LwPosr: Lightweight Efficient Fine-Grained Head Pose Estimation](https://mlsanity.com/paper/2202.03544/) (2022)
- [Mathematical Foundation and Corrections for Full Range Head Pose Estimation](https://mlsanity.com/paper/2403.18104/) (2024)
- [An Intuitive and Unconstrained 2D Cube Representation for Simultaneous Head Detection and Pose Estimation](https://mlsanity.com/paper/2212.03623/) (2022)
- [Semi-Supervised Unconstrained Head Pose Estimation in the Wild](https://mlsanity.com/paper/2404.02544/) (2024)
- [Simultaneous face detection and 360 degree headpose estimation](https://mlsanity.com/paper/2111.11604/) (2021)
- [HeadPosr: End-to-end Trainable Head Pose Estimation using Transformer Encoders](https://mlsanity.com/paper/2202.03548/) (2022)
- [HPE-CogVLM: Advancing Vision Language Models with a Head Pose Grounding Task](https://mlsanity.com/paper/2406.01914/) (2024)
- [Full-range Head Pose Geometric Data Augmentations](https://mlsanity.com/paper/2408.01566/) (2024)
- [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)
- [Ollivier-Ricci Curvature For Head Pose Estimation From a Single Image](https://mlsanity.com/paper/2204.13006/) (2022)
- [Advanced Medical Image Representation for Efficient Processing and Transfer in Multisite Clouds](https://mlsanity.com/paper/2305.15411/) (2023)
- [Automated Deception Detection from Videos: Using End-to-End Learning Based High-Level Features and Classification Approaches](https://mlsanity.com/paper/2307.06625/) (2023)
- [3D Face Alignment Through Fusion of Head Pose Information and Features](https://mlsanity.com/paper/2308.13327/) (2023)
- [Bidirectional Regression for Monocular 6DoF Head Pose Estimation and Reference System Alignment](https://mlsanity.com/paper/2407.14136/) (2024)
- [CLERF: Contrastive LEaRning for Full Range Head Pose Estimation](https://mlsanity.com/paper/2412.02066/) (2024)

## Abstract (arXiv)

This paper proposes to use the three vectors in a rotation matrix as the representation in head pose estimation and develops a new neural network based on the characteristic of such representation. We address two potential issues existed in current head pose estimation works: 1. Public datasets for head pose estimation use either Euler angles or quaternions to annotate data samples. However, both of these annotations have the issue of discontinuity and thus could result in some performance issues in neural network training. 2. Most research works report Mean Absolute Error (MAE) of Euler angles as the measurement of performance. We show that MAE may not reflect the actual behavior especially for the cases of profile views. To solve these two problems, we propose a new annotation method which uses three vectors to describe head poses and a new measurement Mean Absolute Error of Vectors (MAEV) to assess the performance. We also train a new neural network to predict the three vectors with the constraints of orthogonality. Our proposed method achieves state-of-the-art results on both AFLW2000 and BIWI datasets. Experiments show our vector-based annotation method can effectively reduce prediction errors for large pose angles.