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
Latest papers with no code
Boosting Cross-Quality Face Verification using Blind Face Restoration
In recent years, various Blind Face Restoration (BFR) techniques were developed.
Imperceptible Physical Attack against Face Recognition Systems via LED Illumination Modulation
Although face recognition starts to play an important role in our daily life, we need to pay attention that data-driven face recognition vision systems are vulnerable to adversarial attacks.
SqueezerFaceNet: Reducing a Small Face Recognition CNN Even More Via Filter Pruning
The widespread use of mobile devices for various digital services has created a need for reliable and real-time person authentication.
Towards Fair Face Verification: An In-depth Analysis of Demographic Biases
This paper presents an in-depth analysis, with a particular emphasis on the intersectionality of these demographic factors.
A Study on the Impact of Face Image Quality on Face Recognition in the Wild
In this paper, we partition face images into three different quality sets to evaluate the performance of deep learning methods on cross-quality face images in the wild, and then design a human face verification experiment on these cross-quality data.
TinySiamese Network for Biometric Analysis
The accuracy of the fingerprint, gait (NM-angle 180 degree) and face verification tasks was better than the accuracy of a standard Siamese by 0. 87%, 20. 24% and 3. 85% respectively.
Query-Efficient Decision-based Black-Box Patch Attack
In this work, we first explore the decision-based patch attack.
3D-Aware Adversarial Makeup Generation for Facial Privacy Protection
The privacy and security of face data on social media are facing unprecedented challenges as it is vulnerable to unauthorized access and identification.
Human-Machine Comparison for Cross-Race Face Verification: Race Bias at the Upper Limits of Performance?
Two top-performing face recognition systems from the Face Recognition Vendor Test-ongoing performed the same test (7).
Towards Visual Saliency Explanations of Face Verification
In the past years, deep convolutional neural networks have been pushing the frontier of face recognition (FR) techniques in both verification and identification scenarios.
