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
GhostFaceNets: Lightweight Face Recognition Model From Cheap Operations
serengil/deepface
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IEEE Access 2023
The development of deep learning-based biometric models that can be deployed on devices with constrained memory and computational resources has proven to be a significant challenge.
Explainable Model-Agnostic Similarity and Confidence in Face Verification
martlgap/x-face-verification
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This work focuses on explanations for face recognition systems, vital for developers and operators.
Improving Deep Facial Phenotyping for Ultra-rare Disorder Verification Using Model Ensembles
Because of the overall scarcity of patients with ultra-rare disorders, it is infeasible to directly train a model on them.
Mitigating Gender Bias in Face Recognition Using the von Mises-Fisher Mixture Model
In spite of the high performance and reliability of deep learning algorithms in a wide range of everyday applications, many investigations tend to show that a lot of models exhibit biases, discriminating against specific subgroups of the population (e. g. gender, ethnicity).
Cluster and Aggregate: Face Recognition with Large Probe Set
Advances in attention and recurrent modules have led to feature fusion that can model the relationship among the images in the input set.
T2V-DDPM: Thermal to Visible Face Translation using Denoising Diffusion Probabilistic Models
In this paper, we propose a Denoising Diffusion Probabilistic Model (DDPM) based solution for T2V translation specifically for facial images.
Sim2Word: Explaining Similarity with Representative Attribute Words via Counterfactual Explanations
RuoyuChen10/Sim2Word
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ACM Transactions on Multimedia Computing, Communications and Applications 2022
Different from existing models, in this paper, we propose a new interpretation method that explains the image similarity models by salience maps and attribute words.
On Biased Behavior of GANs for Face Verification
ksasi/fairDL
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Datasets for training face verification systems are difficult to obtain and prone to privacy issues.
Controllable and Guided Face Synthesis for Unconstrained Face Recognition
To address this problem, we propose a controllable face synthesis model (CFSM) that can mimic the distribution of target datasets in a style latent space.
Octuplet Loss: Make Face Recognition Robust to Image Resolution
To address this problem, we propose a novel combination of the popular triplet loss to improve robustness against image resolution via fine-tuning of existing face recognition models.
