Search Results for author:
Found 83 papers, 44 papers with code
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.
IEEE BigData 2023 Keystroke Verification Challenge (KVC)
Several neural architectures were proposed by the participants, leading to global Equal Error Rates (EERs) as low as 3. 33% and 3. 61% achieved by the best team respectively in the desktop and mobile scenario, outperforming the current state of the art biometric verification performance for KD.
Model Compression Techniques in Biometrics Applications: A Survey
The development of deep learning algorithms has extensively empowered humanity's task automatization capacity.
Generalization of Fitness Exercise Recognition from Doppler Measurements by Domain-adaption and Few-Shot Learning
In previous works, a mobile application was developed using an unmodified commercial off-the-shelf smartphone to recognize whole-body exercises.
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.
Keystroke Verification Challenge (KVC): Biometric and Fairness Benchmark Evaluation
Analyzing keystroke dynamics (KD) for biometric verification has several advantages: it is among the most discriminative behavioral traits; keyboards are among the most common human-computer interfaces, being the primary means for users to enter textual data; its acquisition does not require additional hardware, and its processing is relatively lightweight; and it allows for transparently recognizing subjects.
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).
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.
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.
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.
Unveiling the Two-Faced Truth: Disentangling Morphed Identities for Face Morphing Detection
Over time they have become simpler to perform and more realistic, as such, the usage of deep learning systems to detect these attacks has grown.
Ranked #1 on
Face Morphing Attack Detection
on FRLL-Morphs
Optimization-Based Improvement of Face Image Quality Assessment Techniques
To help improve the performance and stability of FR systems in such unconstrained settings, face image quality assessment (FIQA) techniques try to infer sample-quality information from the input face images that can aid with the recognition process.
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.
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.
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.
Multispectral Imaging for Differential Face Morphing Attack Detection: A Preliminary Study
The D-MAD methods are based on using two facial images that are captured from the ePassport (also called the reference image) and the trusted device (for example, Automatic Border Control (ABC) gates) to detect whether the face image presented in ePassport is morphed.
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.
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.
Periocular Biometrics: A Modality for Unconstrained Scenarios
Periocular refers to the externally visible region of the face that surrounds the eye socket.
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
A Survey on Computer Vision based Human Analysis in the COVID-19 Era
The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals.
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.
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.
Towards Explaining Demographic Bias through the Eyes of Face Recognition Models
To improve the trustfulness of such ML decision systems, it is crucial to be aware of the inherent biases in these solutions and to make them more transparent to the public and developers.
OrthoMAD: Morphing Attack Detection Through Orthogonal Identity Disentanglement
Morphing attacks are one of the many threats that are constantly affecting deep face recognition systems.
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.
Face Morphing Attacks and Face Image Quality: The Effect of Morphing and the Unsupervised Attack Detection by Quality
A morphed image can be successfully verified to multiple identities.
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.
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.
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.
Explainability of the Implications of Supervised and Unsupervised Face Image Quality Estimations Through Activation Map Variation Analyses in Face Recognition Models
To avoid the low discrimination between the general spatial activation mapping of low and high-quality images in FR models, we build our explainability tools in a higher derivative space by analyzing the variation of the FR activation maps of image sets with different quality decisions.
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.
Ranked #1 on
Face Verification
on IJB-B
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.
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.
A Deep Insight into Measuring Face Image Utility with General and Face-specific Image Quality Metrics
This paper focuses on face images and the measurement of face image utility with general and face-specific image quality metrics.
The Effect of Wearing a Face Mask on Face Image Quality
This work studies, for the first time, the effect of wearing a face mask on face image quality by utilising state-of-the-art face image quality assessment methods of different natures.
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)
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.
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.
MiDeCon: Unsupervised and Accurate Fingerprint and Minutia Quality Assessment based on Minutia Detection Confidence
The implementation is publicly available.
Demographic Fairness in Biometric Systems: What do the Experts say?
Algorithmic decision systems have frequently been labelled as "biased", "racist", "sexist", or "unfair" by numerous media outlets, organisations, and researchers.
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.
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.
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.
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.
Biometrics in the Era of COVID-19: Challenges and Opportunities
Since early 2020 the COVID-19 pandemic has had a considerable impact on many aspects of daily life.
MAAD-Face: A Massively Annotated Attribute Dataset for Face Images
In this work, we propose MAADFace, a new face annotations database that is characterized by the large number of its high-quality attribute annotations.
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.
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.
Beyond Identity: What Information Is Stored in Biometric Face Templates?
For evaluating the predictability of the attributes, we trained a massive attribute classifier that is additionally able to accurately state its prediction confidence.
MIPGAN -- Generating Strong and High Quality Morphing Attacks Using Identity Prior Driven GAN
Extensive experiments are carried out to assess the FRS's vulnerability against the proposed morphed face generation technique on three types of data such as digital images, re-digitized (printed and scanned) images, and compressed images after re-digitization from newly generated MIPGAN Face Morph Dataset.
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.
Can GAN Generated Morphs Threaten Face Recognition Systems Equally as Landmark Based Morphs? -- Vulnerability and Detection
\textit{(i) Can GAN generated morphs threaten Face Recognition Systems (FRS) equally as Landmark based morphs?}
Face Quality Estimation and Its Correlation to Demographic and Non-Demographic Bias in Face Recognition
Face quality assessment aims at estimating the utility of a face image for the purpose of recognition.
SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness
Face image quality is an important factor to enable high performance face recognition systems.
Ranked #1 on
Face Quality Assessement
on LFW
Demographic Bias in Presentation Attack Detection of Iris Recognition Systems
With the widespread use of biometric systems, the demographic bias problem raises more attention.
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.
Post-Comparison Mitigation of Demographic Bias in Face Recognition Using Fair Score Normalization
In contrast to previous works, our fair normalization approach enhances the overall performance by up to 53. 2% at false match rate of 0. 001 and up to 82. 9% at a false match rate of 0. 00001.
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.
