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Found 21 papers, 12 papers with code
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.
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Face Verification
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Pixel-Level Face Image Quality Assessment for Explainable Face Recognition
To achieve this, a model-specific quality value of the input image is estimated and used to build a sample-specific quality regression model.
Mask-invariant Face Recognition through Template-level Knowledge Distillation
The emergence of the global COVID-19 pandemic poses new challenges for biometrics.
SynthASpoof: Developing Face Presentation Attack Detection Based on Privacy-friendly Synthetic Data
To target these legal and technical challenges, this work presents the first synthetic-based face PAD dataset, named SynthASpoof, as a large-scale PAD development dataset.
SFace: Privacy-friendly and Accurate Face Recognition using Synthetic Data
The reported evaluation results on five authentic face benchmarks demonstrated that the privacy-friendly synthetic dataset has high potential to be used for training face recognition models, achieving, for example, a verification accuracy of 91. 87\% on LFW using multi-class classification and 99. 13\% using the combined learning strategy.
MorDIFF: Recognition Vulnerability and Attack Detectability of Face Morphing Attacks Created by Diffusion Autoencoders
Creating morphing attacks is commonly either performed on the image-level or on the representation-level.
Unsupervised Enhancement of Soft-biometric Privacy with Negative Face Recognition
Current research on soft-biometrics showed that privacy-sensitive information can be deduced from biometric templates of an individual.
SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data
The competition attracted a total of 12 participating teams, both from academia and industry and present in 11 different countries.
OrthoMAD: Morphing Attack Detection Through Orthogonal Identity Disentanglement
Morphing attacks are one of the many threats that are constantly affecting deep face recognition systems.
Efficient Explainable Face Verification based on Similarity Score Argument Backpropagation
Explainable Face Recognition is gaining growing attention as the use of the technology is gaining ground in security-critical applications.
SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data
This paper presents a summary of the Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data (SynFacePAD 2023) held at the 2023 International Joint Conference on Biometrics (IJCB 2023).
Model Compression Techniques in Biometrics Applications: A Survey
The development of deep learning algorithms has extensively empowered humanity's task automatization capacity.
A Comprehensive Study on Face Recognition Biases Beyond Demographics
However, to enable a trustworthy FR technology, it is essential to know the influence of an extended range of facial attributes on FR beyond demographics.
Towards Understanding Data Values: Empirical Results on Synthetic Data
Understanding the influence of data on machine learning models is an emerging research field.
Feature Visualization within an Automated Design Assessment leveraging Explainable Artificial Intelligence Methods
Explainable AI methods have been used in order to assess whether a neural network has successfully learned a given task or to analyze which features of an input might lead to an adversarial attack.
On the (Limited) Generalization of MasterFace Attacks and Its Relation to the Capacity of Face Representations
However, previous works followed evaluation settings consisting of older recognition models, limited cross-dataset and cross-model evaluations, and the use of low-scale testing data.
Stating Comparison Score Uncertainty and Verification Decision Confidence Towards Transparent Face Recognition
The confidence of a decision is often based on the overall performance of the model or on the image quality.
Are Explainability Tools Gender Biased? A Case Study on Face Presentation Attack Detection
Face recognition (FR) systems continue to spread in our daily lives with an increasing demand for higher explainability and interpretability of FR systems that are mainly based on deep learning.
Liveness Detection Competition -- Noncontact-based Fingerprint Algorithms and Systems (LivDet-2023 Noncontact Fingerprint)
The competition serves as an important benchmark in noncontact-based fingerprint PAD, offering (a) independent assessment of the state-of-the-art in noncontact-based fingerprint PAD for algorithms and systems, and (b) common evaluation protocol, which includes finger photos of a variety of Presentation Attack Instruments (PAIs) and live fingers to the biometric research community (c) provides standard algorithm and system evaluation protocols, along with the comparative analysis of state-of-the-art algorithms from academia and industry with both old and new android smartphones.
Bias and Diversity in Synthetic-based Face Recognition
In this work, we investigate how the diversity of synthetic face recognition datasets compares to authentic datasets, and how the distribution of the training data of the generative models affects the distribution of the synthetic data.
Second Edition FRCSyn Challenge at CVPR 2024: Face Recognition Challenge in the Era of Synthetic Data
Synthetic data is gaining increasing relevance for training machine learning models.
