Face Alignment
99 papers with code • 26 benchmarks • 17 datasets
Face alignment is the task of identifying the geometric structure of faces in digital images, and attempting to obtain a canonical alignment of the face based on translation, scale, and rotation.
( Image credit: 3DDFA_V2 )
Benchmarks
These leaderboards are used to track progress in Face Alignment
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Libraries
Use these libraries to find Face Alignment models and implementations
CelebA
300W
Helen
AFW
AFLW
LFPW
AFLW2000-3D
COFW
WFLW
FaceScape
Latest papers with no code
A Comparative Analysis of the Face Recognition Methods in Video Surveillance Scenarios
Facial recognition is fundamental for a wide variety of security systems operating in real-time applications.
Exploring StyleGAN Latent Space for Face Alignment with Limited Training Data
In this paper, we propose to use StyleGAN to perform face alignment with limited training data instead of image generation.
Face Shape-Guided Deep Feature Alignment for Face Recognition Robust to Face Misalignment
This is mainly attributed to the mismatch between training and testing sets.
Towards Accurate Facial Landmark Detection via Cascaded Transformers
We formulate facial landmark detection as a coordinate regression task such that the model can be trained end-to-end.
Activation Template Matching Loss for Explainable Face Recognition
Can we construct an explainable face recognition network able to learn a facial part-based feature like eyes, nose, mouth and so forth, without any manual annotation or additionalsion datasets?
Are 3D Face Shapes Expressive Enough for Recognising Continuous Emotions and Action Unit Intensities?
We also study how 3D face shapes performed on AU intensity estimation on BP4D and DISFA datasets, and report that 3D face features were on par with 2D appearance features in AUs 4, 6, 10, 12, and 25, but not the entire set of AUs.
Face Image Lighting Enhancement Using a 3D Model
Finally, we generate an illuminance-balanced face image from a single view.
SCAF: Skip-Connections in Auto-encoder for Face alignment with few annotated data
Supervised face alignment methods need large amounts of training data to achieve good performance in terms of accuracy and generalization.
3D face reconstruction with dense landmarks
By fitting a morphable model to these dense landmarks, we achieve state-of-the-art results for monocular 3D face reconstruction in the wild.
Multi-Domain Multi-Definition Landmark Localization for Small Datasets
Training a small dataset alongside a large(r) dataset helps with robust learning for the former, and provides a universal mechanism for facial landmark localization for new and/or smaller standard datasets.
