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Found 49 papers, 29 papers with code
Frequency Matters: Explaining Biases of Face Recognition in the Frequency Domain
Face recognition (FR) models are vulnerable to performance variations across demographic groups.
MADation: Face Morphing Attack Detection with Foundation Models
Despite the considerable performance improvements of face recognition algorithms in recent years, the same scientific advances responsible for this progress can also be used to create efficient ways to attack them, posing a threat to their secure deployment.
FoundPAD: Foundation Models Reloaded for Face Presentation Attack Detection
Although face recognition systems have seen a massive performance enhancement in recent years, they are still targeted by threats such as presentation attacks, leading to the need for generalizable presentation attack detection (PAD) algorithms.
Second FRCSyn-onGoing: Winning Solutions and Post-Challenge Analysis to Improve Face Recognition with Synthetic Data
In order to promote the proposal of novel Generative AI methods and synthetic data, and investigate the application of synthetic data to better train face recognition systems, we introduce the 2nd FRCSyn-onGoing challenge, based on the 2nd Face Recognition Challenge in the Era of Synthetic Data (FRCSyn), originally launched at CVPR 2024.
FRoundation: Are Foundation Models Ready for Face Recognition?
Even with access to large-scale face recognition training datasets, fine-tuned foundation models perform comparably to models trained from scratch, but with lower training computational costs and without relying on the assumption of extensive data availability.
A Survey on Drowsiness Detection -- Modern Applications and Methods
By examining the multifaceted implications of drowsiness, this work contributes to a holistic understanding of its impact and the crucial role of accurate and real-time detection techniques in enhancing safety and performance.
AdaDistill: Adaptive Knowledge Distillation for Deep Face Recognition
Knowledge distillation (KD) aims at improving the performance of a compact student model by distilling the knowledge from a high-performing teacher model.
GraFIQs: Face Image Quality Assessment Using Gradient Magnitudes
We propose in this work a novel approach to assess the quality of face images based on inspecting the required changes in the pre-trained FR model weights to minimize differences between testing samples and the distribution of the FR training dataset.
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.
AI-KD: Towards Alignment Invariant Face Image Quality Assessment Using Knowledge Distillation
To address this problem, we present in this paper a novel knowledge distillation approach, termed AI-KD that can extend on any existing FIQA technique, improving its robustness to alignment variations and, in turn, performance with different alignment procedures.
SDFR: Synthetic Data for Face Recognition Competition
The submitted models were trained on existing and also new synthetic datasets and used clever methods to improve training with synthetic data.
If It's Not Enough, Make It So: Reducing Authentic Data Demand in Face Recognition through Synthetic Faces
Finally, we assessed the effectiveness of data augmentation approaches on synthetic and authentic data, with the same goal in mind.
FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Data
Despite the widespread adoption of face recognition technology around the world, and its remarkable performance on current benchmarks, there are still several challenges that must be covered in more detail.
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.
IDiff-Face: Synthetic-based Face Recognition through Fizzy Identity-Conditioned Diffusion Models
The availability of large-scale authentic face databases has been crucial to the significant advances made in face recognition research over the past decade.
Ranked #2 on
Synthetic Face Recognition
on AgeDB-30
(Accuracy metric)
EFaR 2023: Efficient Face Recognition Competition
To drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size.
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.
Synthetic Data for Face Recognition: Current State and Future Prospects
Over the past years, deep learning capabilities and the availability of large-scale training datasets advanced rapidly, leading to breakthroughs in face recognition accuracy.
Identity-driven Three-Player Generative Adversarial Network for Synthetic-based Face Recognition
We empirically proved that our IDnet synthetic images are of higher identity discrimination in comparison to the conventional two-player GAN, while maintaining a realistic intra-identity variation.
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.
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.
IDiff-Face: Synthetic-based Face Recognition through Fizzy Identity-Conditioned Diffusion Model
The availability of large-scale authentic face databases has been crucial to the significant advances made in face recognition research over the past decade.
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
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.
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.
OCFR 2022: Competition on Occluded Face Recognition From Synthetically Generated Structure-Aware Occlusions
This work summarizes the IJCB Occluded Face Recognition Competition 2022 (IJCB-OCFR-2022) embraced by the 2022 International Joint Conference on Biometrics (IJCB 2022).
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.
QuantFace: Towards Lightweight Face Recognition by Synthetic Data Low-bit Quantization
Deep learning-based face recognition models follow the common trend in deep neural networks by utilizing full-precision floating-point networks with high computational costs.
Ranked #1 on
Quantization
on LFW
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.
Mask-invariant Face Recognition through Template-level Knowledge Distillation
The emergence of the global COVID-19 pandemic poses new challenges for biometrics.
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.
FocusFace: Multi-task Contrastive Learning for Masked Face Recognition
The proposed architecture is designed to be trained from scratch or to work on top of state-of-the-art face recognition methods without sacrificing the capabilities of a existing models in conventional face recognition tasks.
ElasticFace: Elastic Margin Loss for Deep Face Recognition
The recent state-of-the-art face recognition solutions proposed to incorporate a fixed penalty margin on commonly used classification loss function, softmax loss, in the normalized hypersphere to increase the discriminative power of face recognition models, by minimizing the intra-class variation and maximizing the inter-class variation.
Ranked #1 on
Face Recognition
on IJB-B
(TAR @ FAR=0.0001 metric)
PocketNet: Extreme Lightweight Face Recognition Network using Neural Architecture Search and Multi-Step Knowledge Distillation
However, this limits the deployment of such models that contain an extremely large number of parameters to embedded and low-end devices.
Ranked #2 on
Lightweight Face Recognition
on CALFW
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.
My Eyes Are Up Here: Promoting Focus on Uncovered Regions in Masked Face Recognition
The recent Covid-19 pandemic and the fact that wearing masks in public is now mandatory in several countries, created challenges in the use of face recognition systems (FRS).
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
MFR 2021: Masked Face Recognition Competition
These teams successfully submitted 18 valid solutions.
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.
Self-restrained Triplet Loss for Accurate Masked Face Recognition
Using the face as a biometric identity trait is motivated by the contactless nature of the capture process and the high accuracy of the recognition algorithms.
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.
On Benchmarking Iris Recognition within a Head-mounted Display for AR/VR Application
Motivated by the performance of iris recognition, we also propose the continuous authentication of users in a non-collaborative capture setting in HMD.
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).
The Effect of Wearing a Mask on Face Recognition Performance: an Exploratory Study
The recent COVID-19 pandemic have increased the value of hygienic and contactless identity verification.
Cascaded Generation of High-quality Color Visible Face Images from Thermal Captures
Generating visible-like face images from thermal images is essential to perform manual and automatic cross-spectrum face recognition.
