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
Most implemented papers
Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks
open-mmlab/mmpose
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CVPR 2018
We present a new loss function, namely Wing loss, for robust facial landmark localisation with Convolutional Neural Networks (CNNs).
FacePoseNet: Making a Case for Landmark-Free Face Alignment
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.
FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors
We present a novel deep end-to-end trainable Face Super-Resolution Network (FSRNet), which makes full use of the geometry prior, i. e., facial landmark heatmaps and parsing maps, to super-resolve very low-resolution (LR) face images without well-aligned requirement.
Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network
We propose a straightforward method that simultaneously reconstructs the 3D facial structure and provides dense alignment.
Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry
Our synergy process leverages a representation cycle for 3DMM parameters and 3D landmarks.
Binarized Convolutional Landmark Localizers for Human Pose Estimation and Face Alignment with Limited Resources
1adrianb/binary-networks-pytorch
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ICCV 2017
(d) We present results for experiments on the most challenging datasets for human pose estimation and face alignment, reporting in many cases state-of-the-art performance.
Deep Alignment Network: A convolutional neural network for robust face alignment
MarekKowalski/DeepAlignmentNetwork
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Our method uses entire face images at all stages, contrary to the recently proposed face alignment methods that rely on local patches.
Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment
Face Analysis Project on MXNet
Towards Fast, Accurate and Stable 3D Dense Face Alignment
cleardusk/3DDFA_V2
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ECCV 2020
Firstly, on the basis of a lightweight backbone, we propose a meta-joint optimization strategy to dynamically regress a small set of 3DMM parameters, which greatly enhances speed and accuracy simultaneously.
Faceptor: A Generalist Model for Face Perception
This design enhances the unification of model structure while improving application efficiency in terms of storage overhead.
