# Robust Facial Landmark Detection under Significant Head Poses and Occlusion

arXiv: https://arxiv.org/abs/1709.08127 · HTML: https://mlsanity.com/paper/1709.08127/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2017.

## Built on by (most cited first, 12 of 12)

- [ADNet: Leveraging Error-Bias Towards Normal Direction in Face Alignment](https://mlsanity.com/paper/2109.05721/) (2021)
- [FaceXFormer: A Unified Transformer for Facial Analysis](https://mlsanity.com/paper/2403.12960/) (2024)
- [Multi-task head pose estimation in-the-wild](https://mlsanity.com/paper/2202.02299/) (2022)
- [Towards Robust and Unconstrained Full Range of Rotation Head Pose Estimation](https://mlsanity.com/paper/2309.07654/) (2023)
- [Precise Facial Landmark Detection by Reference Heatmap Transformer](https://mlsanity.com/paper/2303.07840/) (2023)
- [Segmentation-Reconstruction-Guided Facial Image De-occlusion](https://mlsanity.com/paper/2112.08022/) (2021)
- [TUSK: Task-Agnostic Unsupervised Keypoints](https://mlsanity.com/paper/2206.08460/) (2022)
- [2D Image head pose estimation via latent space regression under occlusion settings](https://mlsanity.com/paper/2311.06038/) (2023)
- [ORFormer: Occlusion-Robust Transformer for Accurate Facial Landmark Detection](https://mlsanity.com/paper/2412.13174/) (2024)
- [Heatmap Regression without Soft-Argmax for Facial Landmark Detection](https://mlsanity.com/paper/2508.14929/) (2025)
- [Development of a face mask detection pipeline for mask-wearing monitoring in the era of the COVID-19 pandemic: A modular approach](https://mlsanity.com/paper/2112.15031/) (2021)
- [Latent Embedding Clustering for Occlusion Robust Head Pose Estimation](https://mlsanity.com/paper/2403.20251/) (2024)

## Abstract (arXiv)

There have been tremendous improvements for facial landmark detection on general "in-the-wild" images. However, it is still challenging to detect the facial landmarks on images with severe occlusion and images with large head poses (e.g. profile face). In fact, the existing algorithms usually can only handle one of them. In this work, we propose a unified robust cascade regression framework that can handle both images with severe occlusion and images with large head poses. Specifically, the method iteratively predicts the landmark occlusions and the landmark locations. For occlusion estimation, instead of directly predicting the binary occlusion vectors, we introduce a supervised regression method that gradually updates the landmark visibility probabilities in each iteration to achieve robustness. In addition, we explicitly add occlusion pattern as a constraint to improve the performance of occlusion prediction. For landmark detection, we combine the landmark visibility probabilities, the local appearances, and the local shapes to iteratively update their positions. The experimental results show that the proposed method is significantly better than state-of-the-art works on images with severe occlusion and images with large head poses. It is also comparable to other methods on general "in-the-wild" images.