# Shape Preserving Facial Landmarks with Graph Attention Networks

arXiv: https://arxiv.org/abs/2210.07233 · HTML: https://mlsanity.com/paper/2210.07233/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 45 references that are in the map.
- First posted on arXiv 13 Oct 2022.

## Builds on (most cited first, 27 of 27)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [A simple neural network module for relational reasoning](https://mlsanity.com/paper/1706.01427/) (2017)
- [Fake It Till You Make It: Face analysis in the wild using synthetic data alone](https://mlsanity.com/paper/2109.15102/) (2021)
- [Look at Boundary: A Boundary-Aware Face Alignment Algorithm](https://mlsanity.com/paper/1805.10483/) (2018)
- [Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression](https://mlsanity.com/paper/1904.07399/) (2019)
- [Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks](https://mlsanity.com/paper/1711.06753/) (2017)
- [LUVLi Face Alignment: Estimating Landmarks' Location, Uncertainty, and Visibility Likelihood](https://mlsanity.com/paper/2004.02980/) (2020)
- [Style Aggregated Network for Facial Landmark Detection](https://mlsanity.com/paper/1803.04108/) (2018)
- [Structured Landmark Detection via Topology-Adapting Deep Graph Learning](https://mlsanity.com/paper/2004.08190/) (2020)
- [Sparse Local Patch Transformer for Robust Face Alignment and Landmarks Inherent Relation Learning](https://mlsanity.com/paper/2203.06541/) (2022)
- [DeCaFA: Deep Convolutional Cascade for Face Alignment In The Wild](https://mlsanity.com/paper/1904.02549/) (2019)
- [Deep Alignment Network: A convolutional neural network for robust face alignment](https://mlsanity.com/paper/1706.01789/) (2017)
- [ADNet: Leveraging Error-Bias Towards Normal Direction in Face Alignment](https://mlsanity.com/paper/2109.05721/) (2021)
- [Aggregation via Separation: Boosting Facial Landmark Detector with Semi-Supervised Style Translation](https://mlsanity.com/paper/1908.06440/) (2019)
- [Towards Accurate Facial Landmark Detection via Cascaded Transformers](https://mlsanity.com/paper/2208.10808/) (2022)
- [Multi-task head pose estimation in-the-wild](https://mlsanity.com/paper/2202.02299/) (2022)
- [Disentangling 3D Pose in A Dendritic CNN for Unconstrained 2D Face Alignment](https://mlsanity.com/paper/1802.06713/) (2018)
- [Structured Feature Learning for Pose Estimation](https://mlsanity.com/paper/1603.09065/) (2016)
- [Keypoint-Aligned Embeddings for Image Retrieval and Re-identification](https://mlsanity.com/paper/2008.11368/) (2020)
- [Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation](https://mlsanity.com/paper/1511.07356/) (2015)
- [PropagationNet: Propagate Points to Curve to Learn Structure Information](https://mlsanity.com/paper/2006.14308/) (2020)
- [ASMNet: a Lightweight Deep Neural Network for Face Alignment and Pose Estimation](https://mlsanity.com/paper/2103.00119/) (2021)
- [Face Alignment using a 3D Deeply-initialized Ensemble of Regression Trees](https://mlsanity.com/paper/1902.01831/) (2019)
- [Face Alignment Assisted by Head Pose Estimation](https://mlsanity.com/paper/1507.03148/) (2015)

## Built on by (most cited first, 23 of 23)

- [Stable-Makeup: When Real-World Makeup Transfer Meets Diffusion Model](https://mlsanity.com/paper/2403.07764/) (2024)
- [3D Face Tracking from 2D Video through Iterative Dense UV to Image Flow](https://mlsanity.com/paper/2404.09819/) (2024)
- [Generalizable Face Landmarking Guided by Conditional Face Warping](https://mlsanity.com/paper/2404.12322/) (2024)
- [Cascaded Dual Vision Transformer for Accurate Facial Landmark Detection](https://mlsanity.com/paper/2411.07167/) (2024)
- [OpenFace 3.0: A Lightweight Multitask System for Comprehensive Facial Behavior Analysis](https://mlsanity.com/paper/2506.02891/) (2025)
- [Automated Detection of Cat Facial Landmarks](https://mlsanity.com/paper/2310.09793/) (2023)
- [Measuring Student Behavioral Engagement using Histogram of Actions](https://mlsanity.com/paper/2307.09420/) (2023)
- [Evaluating Driver Readiness in Conditionally Automated Vehicles from Eye-Tracking Data and Head Pose](https://mlsanity.com/paper/2401.11284/) (2024)
- [Graph-Attention Network with Adversarial Domain Alignment for Robust Cross-Domain Facial Expression Recognition](https://mlsanity.com/paper/2512.00641/) (2025)
- [BLANKET: Anonymizing Faces in Infant Video Recordings](https://mlsanity.com/paper/2512.15542/) (2025)
- [Authentic Emotion Mapping: Benchmarking Facial Expressions in Real News](https://mlsanity.com/paper/2404.13493/) (2024)
- [Mitigating Knowledge Discrepancies among Multiple Datasets for Task-agnostic Unified Face Alignment](https://mlsanity.com/paper/2503.22359/) (2025)
- [3D Face Alignment Through Fusion of Head Pose Information and Features](https://mlsanity.com/paper/2308.13327/) (2023)
- [Towards Geometric-Photometric Joint Alignment for Facial Mesh Registration](https://mlsanity.com/paper/2403.02629/) (2024)
- [UP-FacE: User-predictable Fine-grained Face Shape Editing](https://mlsanity.com/paper/2403.13972/) (2024)
- [Kinship Verification through a Forest Neural Network](https://mlsanity.com/paper/2504.18910/) (2025)
- [Assessing the Use of Face Swapping Methods as Face Anonymizers in Videos](https://mlsanity.com/paper/2505.20985/) (2025)
- [Bridging the gap in FER: addressing age bias in deep learning](https://mlsanity.com/paper/2507.07638/) (2025)
- [Beyond Flicker: Detecting Kinematic Inconsistencies for Generalizable Deepfake Video Detection](https://mlsanity.com/paper/2512.04175/) (2025)
- [Detection of Intoxicated Individuals from Facial Video Sequences via a Recurrent Fusion Model](https://mlsanity.com/paper/2512.04536/) (2025)
- [DFIC: Towards a balanced facial image dataset for automatic ICAO compliance verification](https://mlsanity.com/paper/2602.10985/) (2026)
- [Generalized Disguise Makeup Presentation Attack Detection Using an Attention-Guided Patch-Based Framework](https://mlsanity.com/paper/2604.26025/) (2026)
- [NeoLoc-68: End-to-end 68-point neonatal facial landmark localisation in neonatal clinical environments](https://mlsanity.com/paper/2606.20823/) (2026)

## Abstract (arXiv)

Top-performing landmark estimation algorithms are based on exploiting the excellent ability of large convolutional neural networks (CNNs) to represent local appearance. However, it is well known that they can only learn weak spatial relationships. To address this problem, we propose a model based on the combination of a CNN with a cascade of Graph Attention Network regressors. To this end, we introduce an encoding that jointly represents the appearance and location of facial landmarks and an attention mechanism to weigh the information according to its reliability. This is combined with a multi-task approach to initialize the location of graph nodes and a coarse-to-fine landmark description scheme. Our experiments confirm that the proposed model learns a global representation of the structure of the face, achieving top performance in popular benchmarks on head pose and landmark estimation. The improvement provided by our model is most significant in situations involving large changes in the local appearance of landmarks.