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Found 25 papers, 12 papers with code
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).
Iris Liveness Detection Competition (LivDet-Iris) -- The 2023 Edition
New elements in this fifth competition include (1) GAN-generated iris images as a category of presentation attack instruments (PAI), and (2) an evaluation of human accuracy at detecting PAI as a reference benchmark.
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.
Face Presentation Attack Detection by Excavating Causal Clues and Adapting Embedding Statistics
We excavate the causal factors hidden in the high-level representation via counterfactual intervention.
ExFaceGAN: Exploring Identity Directions in GAN's Learned Latent Space for Synthetic Identity Generation
To generate multiple samples of a certain synthetic identity, previous works proposed to disentangle the latent space of GANs by incorporating additional supervision or regularization, enabling the manipulation of certain attributes.
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.
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.
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 Face Recognition using Unlabeled Synthetic Data
In this paper, we propose an unsupervised face recognition model based on unlabeled synthetic data (USynthFace).
Ranked #1 on
Unsupervised face recognition
on LFW
Fairness in Face Presentation Attack Detection
Face recognition (FR) algorithms have been proven to exhibit discriminatory behaviors against certain demographic and non-demographic groups, raising ethical and legal concerns regarding their deployment in real-world scenarios.
Unsupervised Face Morphing Attack Detection via Self-paced Anomaly Detection
However, given variations in the morphing attacks, the performance of supervised MAD solutions drops significantly due to the insufficient diversity and quantity of the existing MAD datasets.
Intra and Cross-spectrum Iris Presentation Attack Detection in the NIR and Visible Domains
Extensive experiments are performed on six NIR and one visible-light iris databases to show the effectiveness and robustness of proposed A-PBS methods.
Privacy-friendly Synthetic Data for the Development of Face Morphing Attack Detectors
The main question this work aims at answering is: "can morphing attack detection (MAD) solutions be successfully developed based on synthetic data?".
CR-FIQA: Face Image Quality Assessment by Learning Sample Relative Classifiability
Based on that, our proposed CR-FIQA uses this paradigm to estimate the face image quality of a sample by predicting its relative classifiability.
Partial Attack Supervision and Regional Weighted Inference for Masked Face Presentation Attack Detection
Our proposed method outperforms established PAD methods in the CRMA database by reducing the mentioned shortcomings when facing masked faces.
Learnable Multi-level Frequency Decomposition and Hierarchical Attention Mechanism for Generalized Face Presentation Attack Detection
With the increased deployment of face recognition systems in our daily lives, face presentation attack detection (PAD) is attracting much attention and playing a key role in securing face recognition systems.
PW-MAD: Pixel-wise Supervision for Generalized Face Morphing Attack Detection
A face morphing attack image can be verified to multiple identities, making this attack a major vulnerability to processes based on identity verification, such as border checks.
ReGenMorph: Visibly Realistic GAN Generated Face Morphing Attacks by Attack Re-generation
Face morphing attacks aim at creating face images that are verifiable to be the face of multiple identities, which can lead to building faulty identity links in operations like border checks.
MixFaceNets: Extremely Efficient Face Recognition Networks
In this paper, we present a set of extremely efficient and high throughput models for accurate face verification, MixFaceNets which are inspired by Mixed Depthwise Convolutional Kernels.
Ranked #3 on
Lightweight Face Recognition
on IJB-C
Iris Presentation Attack Detection by Attention-based and Deep Pixel-wise Binary Supervision Network
Iris presentation attack detection (PAD) plays a vital role in iris recognition systems.
Masked Face Recognition: Human vs. Machine
This work provides a joint evaluation and in-depth analyses of the face verification performance of human experts in comparison to state-of-the-art automatic FR solutions.
Real Masks and Spoof Faces: On the Masked Face Presentation Attack Detection
Face masks have become one of the main methods for reducing the transmission of COVID-19.
Micro Stripes Analyses for Iris Presentation Attack Detection
In this paper, we propose a lightweight framework to detect iris presentation attacks by extracting multiple micro-stripes of expanded normalized iris textures.
Iris Liveness Detection Competition (LivDet-Iris) -- The 2020 Edition
Launched in 2013, LivDet-Iris is an international competition series open to academia and industry with the aim to assess and report advances in iris Presentation Attack Detection (PAD).
Demographic Bias in Presentation Attack Detection of Iris Recognition Systems
With the widespread use of biometric systems, the demographic bias problem raises more attention.
