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
Boosting the Adversarial Transferability of Surrogate Models with Dark Knowledge
Trustworthy-AI-Group/TransferAttack
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This paper proposes a method for training a surrogate model with dark knowledge to boost the transferability of the adversarial examples generated by the surrogate model.
AdaFace: Quality Adaptive Margin for Face Recognition
mk-minchul/adaface
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CVPR 2022
In this work, we introduce another aspect of adaptiveness in the loss function, namely the image quality.
Killing Two Birds with One Stone:Efficient and Robust Training of Face Recognition CNNs by Partial FC
In each iteration, positive class centers and a random subset of negative class centers are selected to compute the margin-based softmax loss.
Unified Negative Pair Generation toward Well-discriminative Feature Space for Face Recognition
The goal of face recognition (FR) can be viewed as a pair similarity optimization problem, maximizing a similarity set $\mathcal{S}^p$ over positive pairs, while minimizing similarity set $\mathcal{S}^n$ over negative pairs.
Portrait Eyeglasses and Shadow Removal by Leveraging 3D Synthetic Data
storymy/take-off-eyeglasses
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CVPR 2022
In this paper, we propose a novel framework to remove eyeglasses as well as their cast shadows from face images.
Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse Input
Trustworthy-AI-Group/TransferAttack
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CVPR 2022
To tackle this limitation, we propose the object-based diverse input (ODI) method that draws an adversarial image on a 3D object and induces the rendered image to be classified as the target class.
Probabilistic Embeddings Revisited
tinkoff-ai/probabilistic-embeddings
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We thus provide a new confidence evaluation benchmark and establish a baseline for future confidence prediction research.
It's All in the Head: Representation Knowledge Distillation through Classifier Sharing
alibaba-miil/headsharingkd
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Such direct methods may be limited in transferring high-order dependencies embedded in the representation vectors, or in handling the capacity gap between the teacher and student models.
QMagFace: Simple and Accurate Quality-Aware Face Recognition
These variabilities can be measured in terms of face image quality which is defined over the utility of a sample for recognition.
MixFace: Improving Face Verification Focusing on Fine-grained Conditions
The performance of face recognition has become saturated for public benchmark datasets such as LFW, CFP-FP, and AgeDB, owing to the rapid advances in CNNs.
