# Fast Facial Landmark Detection and Applications: A Survey

arXiv: https://arxiv.org/abs/2101.10808 · HTML: https://mlsanity.com/paper/2101.10808/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 56 of its 101 references that are in the map.
- First posted on arXiv 12 Jan 2021.

## Builds on (most cited first, 30 of 56)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://mlsanity.com/paper/1807.11164/) (2018)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models](https://mlsanity.com/paper/1708.03999/) (2017)
- [Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods](https://mlsanity.com/paper/1705.07263/) (2017)
- [How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)](https://mlsanity.com/paper/1703.07332/) (2017)
- [Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey](https://mlsanity.com/paper/1801.00553/) (2018)
- [Making Convolutional Networks Shift-Invariant Again](https://mlsanity.com/paper/1904.11486/) (2019)
- [An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution](https://mlsanity.com/paper/1807.03247/) (2018)
- [Few-Shot Adversarial Learning of Realistic Neural Talking Head Models](https://mlsanity.com/paper/1905.08233/) (2019)
- [Deep Face Recognition: A Survey](https://mlsanity.com/paper/1804.06655/) (2018)
- [Deep Facial Expression Recognition: A Survey](https://mlsanity.com/paper/1804.08348/) (2018)
- [DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection](https://mlsanity.com/paper/2001.00179/) (2020)
- [MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices](https://mlsanity.com/paper/1804.07573/) (2018)
- [Look at Boundary: A Boundary-Aware Face Alignment Algorithm](https://mlsanity.com/paper/1805.10483/) (2018)

## Built on by (most cited first, 18 of 18)

- [A Survey on Personalized Content Synthesis with Diffusion Models](https://mlsanity.com/paper/2405.05538/) (2024)
- [Recurrence without Recurrence: Stable Video Landmark Detection with Deep Equilibrium Models](https://mlsanity.com/paper/2304.00600/) (2023)
- [Infinite 3D Landmarks: Improving Continuous 2D Facial Landmark Detection](https://mlsanity.com/paper/2405.20117/) (2024)
- [POPoS: Improving Efficient and Robust Facial Landmark Detection with Parallel Optimal Position Search](https://mlsanity.com/paper/2410.09583/) (2024)
- [A Review on Human Pose Estimation](https://mlsanity.com/paper/2110.06877/) (2021)
- [Face Detection on Mobile: Five Implementations and Analysis](https://mlsanity.com/paper/2205.05572/) (2022)
- [Faster Optimization-Based Meta-Learning Adaptation Phase](https://mlsanity.com/paper/2206.05930/) (2022)
- [RAF: Recursive Adversarial Attacks on Face Recognition Using Extremely Limited Queries](https://mlsanity.com/paper/2207.01149/) (2022)
- [Post-Train Adaptive MobileNet for Fast Anti-Spoofing](https://mlsanity.com/paper/2207.13410/) (2022)
- [Quantum-Assisted Support Vector Regression](https://mlsanity.com/paper/2111.09304/) (2021)
- [Post-Train Adaptive U-Net for Image Segmentation](https://mlsanity.com/paper/2301.06358/) (2023)
- [A store-and-forward cloud-based telemonitoring system for automatic assessing dysarthria evolution in neurological diseases from video-recording analysis](https://mlsanity.com/paper/2309.09038/) (2023)
- [Deep Learning-Based Quasi-Conformal Surface Registration for Partial 3D Faces Applied to Facial Recognition](https://mlsanity.com/paper/2405.09880/) (2024)
- [Efficient Facial Landmark Detection for Embedded Systems](https://mlsanity.com/paper/2407.10228/) (2024)
- [3D2M Dataset: A 3-Dimension diverse Mesh Dataset](https://mlsanity.com/paper/2410.07415/) (2024)
- [Lightweight Facial Landmark Detection in Thermal Images via Multi-Level Cross-Modal Knowledge Transfer](https://mlsanity.com/paper/2510.11128/) (2025)
- [A survey of facial recognition techniques](https://mlsanity.com/paper/2601.06239/) (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)

Dense facial landmark detection is one of the key elements of face processing pipeline. It is used in virtual face reenactment, emotion recognition, driver status tracking, etc. Early approaches were suitable for facial landmark detection in controlled environments only, which is clearly insufficient. Neural networks have shown an astonishing qualitative improvement for in-the-wild face landmark detection problem, and are now being studied by many researchers in the field. Numerous bright ideas are proposed, often complimentary to each other. However, exploration of the whole volume of novel approaches is quite challenging. Therefore, we present this survey, where we summarize state-of-the-art algorithms into categories, provide a comparison of recently introduced in-the-wild datasets (e.g., 300W, AFLW, COFW, WFLW) that contain images with large pose, face occlusion, taken in unconstrained conditions. In addition to quality, applications require fast inference, and preferably on mobile devices. Hence, we include information about algorithm inference speed both on desktop and mobile hardware, which is rarely studied. Importantly, we highlight problems of algorithms, their applications, vulnerabilities, and briefly touch on established methods. We hope that the reader will find many novel ideas, will see how the algorithms are used in applications, which will enable further research.