Face Verification
121 papers with code • 20 benchmarks • 21 datasets
Face Verification is a machine learning task in computer vision that involves determining whether two facial images belong to the same person or not. The task involves extracting features from the facial images, such as the shape and texture of the face, and then using these features to compare and verify the similarity between the images.
( Image credit: Pose-Robust Face Recognition via Deep Residual Equivariant Mapping )
Benchmarks
These leaderboards are used to track progress in Face Verification
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Libraries
Use these libraries to find Face Verification models and implementations
LFW
CASIA-WebFace
MS-Celeb-1M
CK+
IJB-C
MegaFace
IJB-A
IJB-B
VGG Face
Oulu-CASIA
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
Recently, a popular line of research in face recognition is adopting margins in the well-established softmax loss function to maximize class separability.
RetinaFace: Single-stage Dense Face Localisation in the Wild
Face Analysis Project on MXNet
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.
A Light CNN for Deep Face Representation with Noisy Labels
This paper presents a Light CNN framework to learn a compact embedding on the large-scale face data with massive noisy labels.
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 $s_n$.
DeepID3: Face Recognition with Very Deep Neural Networks
Very deep neural networks recently achieved great success on general object recognition because of their superb learning capacity.
Additive Margin Softmax for Face Verification
In this work, we introduce a novel additive angular margin for the Softmax loss, which is intuitively appealing and more interpretable than the existing works.
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.
