Robust Face Recognition
19 papers with code • 0 benchmarks • 4 datasets
Robust face recognition is the task of performing recognition in an unconstrained environment, where there is variation of view-point, scale, pose, illumination and expression of the face images.
( Image credit: MeGlass dataset )
Benchmarks
These leaderboards are used to track progress in Robust Face Recognition
MALF
UFDD
MeGlass
OCFR-LFW
Most implemented papers
Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment
Face Analysis Project on MXNet
SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness
Face image quality is an important factor to enable high performance face recognition systems.
SFace: Sigmoid-Constrained Hypersphere Loss for Robust Face Recognition
zhongyy/SFace
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IEEE Transactions on Image Processing 2021
Deep face recognition has achieved great success due to large-scale training databases and rapidly developing loss functions.
Weakly supervised discriminative feature learning with state information for person identification
KovenYu/state-information
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CVPR 2020
We evaluate our model on unsupervised person re-identification and pose-invariant face recognition.
Complete Face Recovery GAN: Unsupervised Joint Face Rotation and De-Occlusion From a Single-View Image
yeongjoonJu/CFR-GAN
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Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022
In addition, the lack of high-quality paired data remains an obstacle for both methods.
Fast L1-Minimization Algorithms For Robust Face Recognition
L1-minimization refers to finding the minimum L1-norm solution to an underdetermined linear system b=Ax.
Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition
To perform unconstrained face recognition robust to variations in illumination, pose and expression, this paper presents a new scheme to extract "Multi-Directional Multi-Level Dual-Cross Patterns" (MDML-DCPs) from face images.
A Fast and Accurate Unconstrained Face Detector
N2ITN/Face2Vec
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First, a new image feature called Normalized Pixel Difference (NPD) is proposed.
A Comprehensive Survey on Pose-Invariant Face Recognition
The capacity to recognize faces under varied poses is a fundamental human ability that presents a unique challenge for computer vision systems.
Multi-Task Convolutional Neural Network for Pose-Invariant Face Recognition
First, we propose a multi-task Convolutional Neural Network (CNN) for face recognition where identity classification is the main task and pose, illumination, and expression estimations are the side tasks.
