Robust Face Recognition
22 papers with code • 0 benchmarks • 6 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
Most implemented papers
SFace: Sigmoid-Constrained Hypersphere Loss for Robust Face Recognition
Deep face recognition has achieved great success due to large-scale training databases and rapidly developing loss functions.
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.
Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment
Face Analysis Project on MXNet
Weakly supervised discriminative feature learning with state information for person identification
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
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
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.
Person Re-Identification by Multi-Channel Parts-Based CNN With Improved Triplet Loss Function
Person re-identification across cameras remains a very challenging problem, especially when there are no overlapping fields of view between cameras.