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
How Good is ChatGPT at Face Biometrics? A First Look into Recognition, Soft Biometrics, and Explainability
bidalab/chatgpt_facebiometrics
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In particular, we analyze the ability of ChatGPT to perform tasks such as face verification, soft-biometrics estimation, and explainability of the results.
Facial Beauty Analysis Using Distribution Prediction and CNN Ensembles
In addition, deep learning based FBP approaches so far use transfer learning from models trained on general classification tasks such as ImageNet.
KFC: Kinship Verification with Fair Contrastive Loss and Multi-Task Learning
garynlfd/kfc
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Kinship verification is an emerging task in computer vision with multiple potential applications.
Unsupervised Disentangling of Facial Representations with 3D-aware Latent Diffusion Models
Second, we propose a novel representation diffusion model (RDM) to disentangle 3D latent into facial identity and expression.
FaceCoresetNet: Differentiable Coresets for Face Set Recognition
In set-based face recognition, we aim to compute the most discriminative descriptor from an unbounded set of images and videos showing a single person.
Face Morphing Attack Detection with Denoising Diffusion Probabilistic Models
MIvanovska/MAD-DDPM
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International Workshop on Biometrics and Forensics (IWBF) 2023
Existing MAD techniques predominantly rely on discriminative models that learn from examples of bona fide and morphed images and, as a result, often exhibit sub-optimal generalization performance when confronted with unknown types of morphing attacks.
CLIP2Protect: Protecting Facial Privacy using Text-Guided Makeup via Adversarial Latent Search
fahadshamshad/clip2protect
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CVPR 2023
We propose a novel two-step approach for facial privacy protection that relies on finding adversarial latent codes in the low-dimensional manifold of a pretrained generative model.
Confidence Intervals for Error Rates in 1:1 Matching Tasks: Critical Statistical Analysis and Recommendations
Matching algorithms are commonly used to predict matches between items in a collection.
Efficient Explainable Face Verification based on Similarity Score Argument Backpropagation
marcohuber/xssab
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Explainable Face Recognition is gaining growing attention as the use of the technology is gaining ground in security-critical applications.
Tackling Face Verification Edge Cases: In-Depth Analysis and Human-Machine Fusion Approach
martlgap/human-machine-fusion
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Finally, we demonstrate that combining machine and human decisions can further improve the performance of state-of-the-art face verification systems on various benchmark datasets.
