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Found 32 papers, 7 papers with code
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.
\textit{sweet} -- An Open Source Modular Platform for Contactless Hand Vascular Biometric Experiments
In this work we present a contactless vascular biometrics sensor platform named \sweet which can be used for hand vascular biometrics studies (wrist-, palm- and finger-vein) and surface features such as palmprint.
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.
Deep Privacy Funnel Model: From a Discriminative to a Generative Approach with an Application to Face Recognition
In this study, we apply the information-theoretic Privacy Funnel (PF) model to the domain of face recognition, developing a novel method for privacy-preserving representation learning within an end-to-end training framework.
Model Pairing Using Embedding Translation for Backdoor Attack Detection on Open-Set Classification Tasks
Backdoor attacks allow an attacker to embed a specific vulnerability in a machine learning algorithm, activated when an attacker-chosen pattern is presented, causing a specific misprediction.
Approximating Optimal Morphing Attacks using Template Inversion
Recent works have demonstrated the feasibility of inverting face recognition systems, enabling to recover convincing face images using only their embeddings.
Deep Variational Privacy Funnel: General Modeling with Applications in Face Recognition
In this study, we harness the information-theoretic Privacy Funnel (PF) model to develop a method for privacy-preserving representation learning using an end-to-end training framework.
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.
SynthDistill: Face Recognition with Knowledge Distillation from Synthetic Data
While generating synthetic datasets for training face recognition models is an alternative option, it is challenging to generate synthetic data with sufficient intra-class variations.
Ranked #1 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.
The Age of Synthetic Realities: Challenges and Opportunities
Synthetic realities are digital creations or augmentations that are contextually generated through the use of Artificial Intelligence (AI) methods, leveraging extensive amounts of data to construct new narratives or realities, regardless of the intent to deceive.
Benchmarking of Cancelable Biometrics for Deep Templates
In this paper, we benchmark several cancelable biometrics (CB) schemes on different biometric characteristics.
Template Inversion Attack against Face Recognition Systems using 3D Face Reconstruction
Then, the extracted templates are stored in the system's database during the enrollment stage and are later used for recognition.
On the detection of morphing attacks generated by GANs
Recent works have demonstrated the feasibility of GAN-based morphing attacks that reach similar success rates as more traditional landmark-based methods.
An anomaly detection approach for backdoored neural networks: face recognition as a case study
Backdoor attacks allow an attacker to embed functionality jeopardizing proper behavior of any algorithm, machine learning or not.
Eight Years of Face Recognition Research: Reproducibility, Achievements and Open Issues
With the popularity of deep learning and its capability to solve a huge variety of different problems, face recognition researchers have concentrated effort on creating better models under this paradigm.
Are GAN-based Morphs Threatening Face Recognition?
Morphing attacks are a threat to biometric systems where the biometric reference in an identity document can be altered.
MLP-Hash: Protecting Face Templates via Hashing of Randomized Multi-Layer Perceptron
Applications of face recognition systems for authentication purposes are growing rapidly.
Biometric Template Protection for Neural-Network-based Face Recognition Systems: A Survey of Methods and Evaluation Techniques
So, we recommend a greater focus on empirical evaluations to provide more concrete insights into the irreversibility and renewability/unlinkability of face BTP methods in practice.
Towards Protecting Face Embeddings in Mobile Face Verification Scenarios
Results indicate that PolyProtect can be tuned to achieve a satisfactory trade-off between the recognition accuracy of the PolyProtected face verification system and the irreversibility of the PolyProtected templates.
Master Face Attacks on Face Recognition Systems
Previous work has proven the existence of master faces, i. e., faces that match multiple enrolled templates in face recognition systems, and their existence extends the ability of presentation attacks.
On the use of automatically generated synthetic image datasets for benchmarking face recognition
The availability of large-scale face datasets has been key in the progress of face recognition.
Vulnerability Analysis of Face Morphing Attacks from Landmarks and Generative Adversarial Networks
Morphing attacks is a threat to biometric systems where the biometric reference in an identity document can be altered.
Fairness in Biometrics: a figure of merit to assess biometric verification systems
Machine learning-based (ML) systems are being largely deployed since the last decade in a myriad of scenarios impacting several instances in our daily lives.
Deepfake detection: humans vs. machines
In response to the threat such manipulations can pose to our trust in video evidence, several large datasets of deepfake videos and many methods to detect them were proposed recently.
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).
Generating Master Faces for Use in Performing Wolf Attacks on Face Recognition Systems
In this work, we demonstrated that wolf (generic) faces, which we call "master faces," can also compromise face recognition systems and that the master face concept can be generalized in some cases.
Multispectral Biometrics System Framework: Application to Presentation Attack Detection
In this work, we present a general framework for building a biometrics system capable of capturing multispectral data from a series of sensors synchronized with active illumination sources.
Smartphone Multi-modal Biometric Authentication: Database and Evaluation
One of the unique features of this dataset is that it is collected in four different geographic locations representing a diverse population and ethnicity.
The Speed Submission to DIHARD II: Contributions & Lessons Learned
This paper describes the speaker diarization systems developed for the Second DIHARD Speech Diarization Challenge (DIHARD II) by the Speed team.
Vulnerability of Face Recognition to Deep Morphing
We show that the state of the art face recognition systems based on VGG and Facenet neural networks are vulnerable to the deep morph videos, with 85. 62 and 95. 00 false acceptance rates, respectively, which means methods for detecting these videos are necessary.
A Reproducible Study on Remote Heart Rate Measurement
This paper studies the problem of reproducible research in remote photoplethysmography (rPPG).
