Facial Landmark Detection

46 papers with code • 7 benchmarks • 13 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 )


Use these libraries to find Facial Landmark Detection models and implementations
2 papers

Most implemented papers

High-Resolution Representations for Labeling Pixels and Regions

leoxiaobin/deep-high-resolution-net.pytorch 9 Apr 2019

The proposed approach achieves superior results to existing single-model networks on COCO object detection.

PFLD: A Practical Facial Landmark Detector

polarisZhao/PFLD-pytorch 28 Feb 2019

Being accurate, efficient, and compact is essential to a facial landmark detector for practical use.

FacePoseNet: Making a Case for Landmark-Free Face Alignment

fengju514/Face-Pose-Net 24 Aug 2017

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

esimov/pigo 26 Mar 2014

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

wywu/LAB CVPR 2018

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

justusschock/shapenet 9 Feb 2019

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 22 Dec 2019

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

jhb86253817/PIPNet 8 Mar 2020

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

jin-s13/COCO-WholeBody ECCV 2020

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.