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
Face Swap via Diffusion Model
This technical report presents a diffusion model based framework for face swapping between two portrait images.
Fiducial Focus Augmentation for Facial Landmark Detection
To effectively utilize the newly proposed augmentation technique, we employ a Siamese architecture-based training mechanism with a Deep Canonical Correlation Analysis (DCCA)-based loss to achieve collective learning of high-level feature representations from two different views of the input images.
3D Face Alignment Through Fusion of Head Pose Information and Features
We experimentally assessed the correlation between the predicted facial landmarks and head pose information, as well as variations in the accuracy of facial landmarks with respect to the quality of head pose information.
Facial Landmark Detection Evaluation on MOBIO Database
Our work is first to perform facial landmark detection evaluation on the mobile still data, i. e., face images from MOBIO database.
Mask Attack Detection Using Vascular-weighted Motion-robust rPPG Signals
Detecting 3D mask attacks to a face recognition system is challenging.
ASM: Adaptive Skinning Model for High-Quality 3D Face Modeling
The research fields of parametric face model and 3D face reconstruction have been extensively studied.
Multi-modal Facial Action Unit Detection with Large Pre-trained Models for the 5th Competition on Affective Behavior Analysis in-the-wild
Facial action unit detection has emerged as an important task within facial expression analysis, aimed at detecting specific pre-defined, objective facial expressions, such as lip tightening and cheek raising.
FreeEnricher: Enriching Face Landmarks without Additional Cost
To evaluate our method, we manually label the dense landmarks on 300W testset.
Improving Depression estimation from facial videos with face alignment, training optimization and scheduling
Deep learning models have shown promising results in recognizing depressive states using video-based facial expressions.
FAN-Trans: Online Knowledge Distillation for Facial Action Unit Detection
Our model consists of a hybrid network of convolution and transformer blocks to learn per-AU features and to model AU co-occurrences.
