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
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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
Latest papers
FaceXFormer: A Unified Transformer for Facial Analysis
Unlike these conventional methods, our FaceXformer leverages a transformer-based encoder-decoder architecture where each task is treated as a learnable token, enabling the integration of multiple tasks within a single framework.
STAR Loss: Reducing Semantic Ambiguity in Facial Landmark Detection
zhenglinzhou/star
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CVPR 2023
To solve this problem, we propose a Self-adapTive Ambiguity Reduction (STAR) loss by exploiting the properties of semantic ambiguity.
KeyPosS: Plug-and-Play Facial Landmark Detection through GPS-Inspired True-Range Multilateration
zhiqic/keyposs
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By spearheading the integration of Multilateration with facial analysis, KeyPosS marks a paradigm shift in facial landmark detection.
ArtFacePoints: High-resolution Facial Landmark Detection in Paintings and Prints
Facial landmark detection plays an important role for the similarity analysis in artworks to compare portraits of the same or similar artists.
Shape Preserving Facial Landmarks with Graph Attention Networks
Top-performing landmark estimation algorithms are based on exploiting the excellent ability of large convolutional neural networks (CNNs) to represent local appearance.
ACR Loss: Adaptive Coordinate-based Regression Loss for Face Alignment
aliprf/acr-loss
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Heatmap-based Regression (HBR) and Coordinate-based Regression (CBR) are among the two mainly used methods for face alignment.
Facial Landmark Points Detection Using Knowledge Distillation-Based Neural Networks
aliprf/kd-loss
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We use two Teacher networks, a Tolerant-Teacher and a Tough-Teacher in conjunction with the Student network.
Towards Fine-grained Image Classification with Generative Adversarial Networks and Facial Landmark Detection
mahdi-darvish/gans-augmented-pet-classifier
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To encounter this problem, we made the best use of GAN-based data augmentation to generate extra dataset instances.
HPRNet: Hierarchical Point Regression for Whole-Body Human Pose Estimation
Differently, in whole-body pose estimation, the locations of fine-grained keypoints (68 on face, 21 on each hand and 3 on each foot) are estimated as well, which creates a scale variance problem that needs to be addressed.
Improving Robustness of Facial Landmark Detection by Defending Against Adversarial Attacks
We argue that exploring the weaknesses of the detector so as to remedy them is a promising method of robust facial landmark detection.
