Facial Landmark Detection
47 papers with code • 10 benchmarks • 16 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 with no code
Real-Time Facial Expression Recognition using Facial Landmarks and Neural Networks
Then, the human face is split into upper and lower faces, which enables the extraction of the desired features from each part.
Robust and Precise Facial Landmark Detection by Self-Calibrated Pose Attention Network
We show that by integrating the BALI fields and SCPA model into a novel self-calibrated pose attention network, more facial prior knowledge can be learned and the detection accuracy and robustness of our method for faces with large poses and heavy occlusions have been improved.
Benchmarking Shadow Removal for Facial Landmark Detection and Beyond
The observation of this work motivates us to design a novel detection-aware shadow removal framework, which empowers shadow removal to achieve higher restoration quality and enhance the shadow robustness of deployed facial landmark detectors.
Quantum-Assisted Support Vector Regression for Detecting Facial Landmarks
Our method is to derive a quadratic-unconstrained-binary formulation for the optimisation problem used for training a SVR model and solve this problem using annealing.
Facial Anatomical Landmark Detection using Regularized Transfer Learning with Application to Fetal Alcohol Syndrome Recognition
This imaging application is characterized by large variations in data appearance and limited availability of labeled data.
DeepFake Detection with Inconsistent Head Poses: Reproducibility and Analysis
Existing literature suggests that this method is an effective DeepFake detector, and its motivating principles are attractively simple.
Zoo-Tuning: Adaptive Transfer from a Zoo of Models
We propose \emph{Zoo-Tuning} to address these challenges, which learns to adaptively transfer the parameters of pretrained models to the target task.
When Liebig's Barrel Meets Facial Landmark Detection: A Practical Model
To this end, we first propose a baseline model equipped with one transformer decoder as detection head.
Few-Shot Model Adaptation for Customized Facial Landmark Detection, Segmentation, Stylization and Shadow Removal
Thus, there is always a great demand in customized data annotations.
CelebHair: A New Large-Scale Dataset for Hairstyle Recommendation based on CelebA
In this paper, we present a new large-scale dataset for hairstyle recommendation, CelebHair, based on the celebrity facial attributes dataset, CelebA.
