Face Recognition
403 papers with code • 19 benchmarks • 77 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
IJB-B
Adience
VGG Face
FairFace
Color FERET
Most implemented papers
FaceNet: A Unified Embedding for Face Recognition and Clustering
davidsandberg/facenet
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CVPR 2015
On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99. 63%.
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
deepinsight/insightface
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CVPR 2019
One of the main challenges in feature learning using Deep Convolutional Neural Networks (DCNNs) for large-scale face recognition is the design of appropriate loss functions that enhance discriminative power.
VGGFace2: A dataset for recognising faces across pose and age
The dataset was collected with three goals in mind: (i) to have both a large number of identities and also a large number of images for each identity; (ii) to cover a large range of pose, age and ethnicity; and (iii) to minimize the label noise.
SphereFace: Deep Hypersphere Embedding for Face Recognition
wy1iu/sphereface
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CVPR 2017
This paper addresses deep face recognition (FR) problem under open-set protocol, where ideal face features are expected to have smaller maximal intra-class distance than minimal inter-class distance under a suitably chosen metric space.
Learning Face Representation from Scratch
The current situation in the field of face recognition is that data is more important than algorithm.
MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
In this paper, we design a benchmark task and provide the associated datasets for recognizing face images and link them to corresponding entity keys in a knowledge base.
Circle Loss: A Unified Perspective of Pair Similarity Optimization
layumi/Person_reID_baseline_pytorch
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CVPR 2020
This paper provides a pair similarity optimization viewpoint on deep feature learning, aiming to maximize the within-class similarity $s_p$ and minimize the between-class similarity $s_n$.
RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition
DingXiaoH/RepMLP
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We propose RepMLP, a multi-layer-perceptron-style neural network building block for image recognition, which is composed of a series of fully-connected (FC) layers.
Can we still avoid automatic face detection?
cydonia999/Tiny_Faces_in_Tensorflow
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In this setting, is it still possible for privacy-conscientious users to avoid automatic face detection and recognition?
CosFace: Large Margin Cosine Loss for Deep Face Recognition
PaddlePaddle/PaddleClas
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CVPR 2018
The central task of face recognition, including face verification and identification, involves face feature discrimination.
