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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
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
V-MAD: Video-based Morphing Attack Detection in Operational Scenarios
In response to the rising threat of the face morphing attack, this paper introduces and explores the potential of Video-based Morphing Attack Detection (V-MAD) systems in real-world operational scenarios.
Explainable Face Verification via Feature-Guided Gradient Backpropagation
Recent years have witnessed significant advancement in face recognition (FR) techniques, with their applications widely spread in people's lives and security-sensitive areas.
Utilizing GANs for Fraud Detection: Model Training with Synthetic Transaction Data
Anomaly detection is a critical challenge across various research domains, aiming to identify instances that deviate from normal data distributions.
Bridging Human Concepts and Computer Vision for Explainable Face Verification
With Artificial Intelligence (AI) influencing the decision-making process of sensitive applications such as Face Verification, it is fundamental to ensure the transparency, fairness, and accountability of decisions.
EFHQ: Multi-purpose ExtremePose-Face-HQ dataset
The existing facial datasets, while having plentiful images at near frontal views, lack images with extreme head poses, leading to the downgraded performance of deep learning models when dealing with profile or pitched faces.
Unsupervised Deep Learning Image Verification Method
In this work, we propose a method to narrow this gap by leveraging an autoencoder to convert the face image vector into a novel representation.
Deep Learning Based Face Recognition Method using Siamese Network
Achieving state-of-the-art results in face verification systems typically hinges on the availability of labeled face training data, a resource that often proves challenging to acquire in substantial quantities.
Autoencoder Based Face Verification System
Initially, an autoencoder is trained in an unsupervised manner using a substantial amount of unlabeled training dataset.
Efficient Verification-Based Face Identification
We study the problem of performing face verification with an efficient neural model $f$.
Generalized Attacks on Face Verification Systems
This paper provides an in-depth study of attacks on FV systems.
