Face Verification

105 papers with code • 21 benchmarks • 22 datasets

Face verification is the task of comparing a candidate face to another, and verifying whether it is a match. It is a one-to-one mapping: you have to check if this person is the correct one.

( Image credit: Pose-Robust Face Recognition via Deep Residual Equivariant Mapping )

Libraries

Use these libraries to find Face Verification models and implementations

Most implemented papers

FaceNet: A Unified Embedding for Face Recognition and Clustering

davidsandberg/facenet 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 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.

VGGFace2: A dataset for recognising faces across pose and age

deepinsight/insightface 23 Oct 2017

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 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.

Circle Loss: A Unified Perspective of Pair Similarity Optimization

layumi/Person_reID_baseline_pytorch 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 $s_n$.

A Light CNN for Deep Face Representation with Noisy Labels

AlfredXiangWu/face_verification_experiment 9 Nov 2015

This paper presents a Light CNN framework to learn a compact embedding on the large-scale face data with massive noisy labels.

Additive Margin Softmax for Face Verification

happynear/AMSoftmax 17 Jan 2018

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 CVPR 2018

The central task of face recognition, including face verification and identification, involves face feature discrimination.