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
Most implemented papers
Supervision-by-Registration: An Unsupervised Approach to Improve the Precision of Facial Landmark Detectors
facebookresearch/supervision-by-registration
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CVPR 2018
In this paper, we present supervision-by-registration, an unsupervised approach to improve the precision of facial landmark detectors on both images and video.
A Deeply-initialized Coarse-to-fine Ensemble of Regression Trees for Face Alignment
bobetocalo/bobetocalo_eccv18
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ECCV 2018
In this paper we present DCFE, a real-time facial landmark regression method based on a coarse-to-fine Ensemble of Regression Trees (ERT).
Can Synthetic Faces Undo the Damage of Dataset Bias to Face Recognition and Facial Landmark Detection?
We observe the following positive effects for face recognition and facial landmark detection tasks: 1) Priming with synthetic face images improves the performance consistently across all benchmarks because it reduces the negative effects of biases in the training data.
Face Alignment using a 3D Deeply-initialized Ensemble of Regression Trees
bobetocalo/bobetocalo_eccv18
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In this paper we present 3DDE, a robust and efficient face alignment algorithm based on a coarse-to-fine cascade of ensembles of regression trees.
Adaloss: Adaptive Loss Function for Landmark Localization
mikgur/MADE_CV_1000_facial_landmarks
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Landmark localization is a challenging problem in computer vision with a multitude of applications.
Aggregation via Separation: Boosting Facial Landmark Detector with Semi-Supervised Style Translation
thesouthfrog/stylealign
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ICCV 2019
Facial landmark detection, or face alignment, is a fundamental task that has been extensively studied.
Unsupervised Learning of Landmarks by Descriptor Vector Exchange
Equivariance to random image transformations is an effective method to learn landmarks of object categories, such as the eyes and the nose in faces, without manual supervision.
Cascade of Encoder-Decoder CNNs with Learned Coordinates Regressor for Robust Facial Landmarks Detection
bobetocalo/bobetocalo_prl19
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Pattern Recognition Letters 2019
In this paper we investigate the use of a cascade of Neural Net regressors to increase the accuracy of the estimated facial landmarks.
FAB: A Robust Facial Landmark Detection Framework for Motion-Blurred Videos
A structure predictor is proposed to predict the missing face structural information temporally, which serves as a geometry prior.
3FabRec: Fast Few-shot Face alignment by Reconstruction
Current supervised methods for facial landmark detection require a large amount of training data and may suffer from overfitting to specific datasets due to the massive number of parameters.
