Facial Landmark Detection
47 papers with code • 9 benchmarks • 15 datasets
Facial Landmark Detection is a computer vision task that involves detecting and localizing specific points or landmarks on a face, such as the eyes, nose, mouth, and chin. The goal is to accurately identify these landmarks in images or videos of faces in real-time and use them for various applications, such as face recognition, facial expression analysis, and head pose estimation.
( Image credit: Style Aggregated Network for Facial Landmark Detection )
Benchmarks
These leaderboards are used to track progress in Facial Landmark Detection
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Facial Landmark Detection models and implementations
300W
Helen
AFW
AFLW
LFPW
AFLW2000-3D
COFW
WFLW
COCO-WholeBody
300-VW
Most implemented papers
High-Resolution Representations for Labeling Pixels and Regions
leoxiaobin/deep-high-resolution-net.pytorch
•
•
The proposed approach achieves superior results to existing single-model networks on COCO object detection.
PFLD: A Practical Facial Landmark Detector
Being accurate, efficient, and compact is essential to a facial landmark detector for practical use.
FacePoseNet: Making a Case for Landmark-Free Face Alignment
Instead, we compare our FPN with existing methods by evaluating how they affect face recognition accuracy on the IJB-A and IJB-B benchmarks: using the same recognition pipeline, but varying the face alignment method.
Fast Localization of Facial Landmark Points
We describe a method that can accurately estimate the positions of relevant facial landmarks in real-time even on hardware with limited processing power, such as mobile devices.
Look at Boundary: A Boundary-Aware Face Alignment Algorithm
By utilising boundary information of 300-W dataset, our method achieves 3. 92% mean error with 0. 39% failure rate on COFW dataset, and 1. 25% mean error on AFLW-Full dataset.
Super-realtime facial landmark detection and shape fitting by deep regression of shape model parameters
We present a method for highly efficient landmark detection that combines deep convolutional neural networks with well established model-based fitting algorithms.
Teacher Supervises Students How to Learn From Partially Labeled Images for Facial Landmark Detection
D-X-Y/landmark-detection
•
•
ICCV 2019
A typical approach is to (1) train a detector on the labeled images; (2) generate new training samples using this detector's prediction as pseudo labels of unlabeled images; (3) retrain the detector on the labeled samples and partial pseudo labeled samples.
Learning to Impute: A General Framework for Semi-supervised Learning
VICO-UoE/L2I
•
•
Recent semi-supervised learning methods have shown to achieve comparable results to their supervised counterparts while using only a small portion of labels in image classification tasks thanks to their regularization strategies.
Pixel-in-Pixel Net: Towards Efficient Facial Landmark Detection in the Wild
The proposed model is equipped with a novel detection head based on heatmap regression, which conducts score and offset predictions simultaneously on low-resolution feature maps.
Whole-Body Human Pose Estimation in the Wild
This paper investigates the task of 2D human whole-body pose estimation, which aims to localize dense landmarks on the entire human body including face, hands, body, and feet.
