Face Recognition
558 papers with code • 22 benchmarks • 61 datasets
Facial Recognition is the task of making a positive identification of a face in a photo or video image against a pre-existing database of faces. It begins with detection - distinguishing human faces from other objects in the image - and then works on identification of those detected faces.
The state of the art tables for this task are contained mainly in the consistent parts of the task : the face verification and face identification tasks.
( Image credit: Face Verification )
Benchmarks
These leaderboards are used to track progress in Face Recognition
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Libraries
Use these libraries to find Face Recognition models and implementationsDatasets
LFW
CASIA-WebFace
MS-Celeb-1M
Extended Yale B
MORPH
IJB-B
Adience
VGG Face
RFW
Partial-REID
Subtasks
Most implemented papers
Regressing Robust and Discriminative 3D Morphable Models with a very Deep Neural Network
The 3D shapes of faces are well known to be discriminative.
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.
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks
ashafahi/inceptionv3-transferLearn-poison
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NeurIPS 2018
The proposed attacks use "clean-labels"; they don't require the attacker to have any control over the labeling of training data.
Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition
XiaohangZhan/cdp
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ECCV 2018
Face recognition has witnessed great progress in recent years, mainly attributed to the high-capacity model designed and the abundant labeled data collected.
Searching Central Difference Convolutional Networks for Face Anti-Spoofing
Here we propose a novel frame level FAS method based on Central Difference Convolution (CDC), which is able to capture intrinsic detailed patterns via aggregating both intensity and gradient information.
Deep Spatial Gradient and Temporal Depth Learning for Face Anti-spoofing
clks-wzz/FAS-SGTD
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CVPR 2020
Depth supervised learning has been proven as one of the most effective methods for face anti-spoofing.
Killing Two Birds with One Stone:Efficient and Robust Training of Face Recognition CNNs by Partial FC
In each iteration, positive class centers and a random subset of negative class centers are selected to compute the margin-based softmax loss.
AdaFace: Quality Adaptive Margin for Face Recognition
mk-minchul/adaface
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CVPR 2022
In this work, we introduce another aspect of adaptiveness in the loss function, namely the image quality.
GroupFace: Learning Latent Groups and Constructing Group-based Representations for Face Recognition
In the field of face recognition, a model learns to distinguish millions of face images with fewer dimensional embedding features, and such vast information may not be properly encoded in the conventional model with a single branch.
Deep Polynomial Neural Networks
grigorisg9gr/polynomial_nets
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We introduce three tensor decompositions that significantly reduce the number of parameters and show how they can be efficiently implemented by hierarchical neural networks.
