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Mirror-Based Full-View Finger Vein Authentication With Illumination Adaptation
Junduan Huang;Zifeng Li;Sushil Bhattacharjee;Sébastien Marcel;Wenxiong Kang
Full-view finger vein (FV) biometrics systems capture multiple FV images of the presented finger ensuring that the entire surface of the finger is covered. Existing full-view FV systems suffer from three common problems: large device size, high cost for multi-camera system, and sub-optimal illumination in the recorded FV images. To address the problem of device size, we propose a novel Mirror-base...Show More
Knowledge Distillation for Face Recognition Using Synthetic Data With Dynamic Latent Sampling
State-of-the-art face recognition models are computationally expensive for mobile applications. Training lightweight face recognition models also requires large identity-labeled datasets, raising privacy and ethical concerns. Generating synthetic datasets for training is also challenging, and there is a significant gap in performance between models trained on real and synthetic face datasets. We p...Show More
Evaluating the Effectiveness of Attack-Agnostic Features for Morphing Attack Detection
2024 IEEE International Joint Conference on Biometrics (IJCB)
Year: 2024 | Conference Paper |
Morphing attacks have diversified significantly over the past years, with new methods based on generative adversarial networks (GANs) and diffusion models posing substantial threats to face recognition systems. Recent research has demonstrated the effectiveness of features extracted from large vision models pretrained on bonafide data only (attack-agnostic features) for detecting deep generative i...Show More
Assessing the Reliability of Biometric Authentication on Virtual Reality Devices
2024 IEEE International Joint Conference on Biometrics (IJCB)
Year: 2024 | Conference Paper |
Recent developments in Virtual Reality (VR) headsets have unlocked a plethora of innovative use-cases, many of which were previously unimaginable. However, as these use-cases, such as personalized immersive experiences, necessitate user authentication, ensuring robustness and resistance to spoofing attacks becomes imperative. The absence of appropriate dataset has constrained our understanding and...Show More
Score Normalization for Demographic Fairness in Face Recognition
Yu Linghu;Tiago de Freitas Pereira;Christophe Ecabert;Sébastien Marcel;Manuel Günther
2024 IEEE International Joint Conference on Biometrics (IJCB)
Year: 2024 | Conference Paper |
Fair biometric algorithms have similar verification performance across different demographic groups given a single decision threshold. Unfortunately, for stateof-the-art face recognition networks, score distributions differ between demographics. Contrary to work that tries to align those distributions by extra training or fine-tuning, we solely focus on score post-processing methods. As proved, we...Show More
Demographic Fairness Transformer for Bias Mitigation in Face Recognition
2024 IEEE International Joint Conference on Biometrics (IJCB)
Year: 2024 | Conference Paper |
Demographic bias in deep learning-based face recognition systems has led to serious concerns. Often, the biased nature of models is attributed to severely imbalanced datasets used for training. However, several studies have shown that biased models can emerge even when trained on balanced data due to factors in the data acquisition process. Considering the impact of input data on demographic bias,...Show More
A Novel and Responsible Dataset for Face Presentation Attack Detection on Mobile Devices
Nathan Ramoly;Alain Komaty;Vedrana Krivokuća Hahn;Lara Younes;Ahmad-Montaser Awal;Sébastien Marcel
2024 IEEE International Joint Conference on Biometrics (IJCB)
Year: 2024 | Conference Paper |
Presentation Attack Detection (PAD) is essential for ensuring the security of face recognition (FR) systems, particularly in the context of mobile authentication in various sectors, such as online banking and government services. However, current PAD methods are often sensitive to the data domain, partly due to the limitations of training PAD datasets. In this paper, we introduce the SO-TERIA data...Show More
Modality Agnostic Heterogeneous Face Recognition with Switch Style Modulators
2024 IEEE International Joint Conference on Biometrics (IJCB)
Year: 2024 | Conference Paper |
Heterogeneous Face Recognition (HFR) systems aim to enhance the capability of face recognition in challenging cross-modal authentication scenarios. However, the significant domain gap between the source and target modalities poses a considerable challenge for cross-domain matching. Existing literature primarily focuses on developing HFR approaches for specific pairs of face modalities, necessitati...Show More
With the rapid development of biometric recognition systems, users can be simultaneously enrolled in multiple biometric recognition systems, either with a single or multiple biometric characteristics (e.g., face, voice, etc.). With such a growth of biometric systems, it is important to secure the sensitive information used within these systems. In particular, considering the privacy issues in such...Show More
Breaking Template Protection: Reconstruction of Face Images from Protected Facial Templates
2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
Face recognition systems tend toward ubiquity and are commonly utilized for security purposes. These systems operate based on facial representations, called templates, extracted by a deep neural network from each face image. However, it has been shown that face recognition templates can be inverted to reconstruct underlying face images, posing new security and privacy threats to face recognition s...Show More
Group Membership Verification via Nonlinear Sparsifying Transform Learning
Behrooz Razeghi;Marzieh Gheisari;Amir Atashin;Dimche Kostadinov;Sébastien Marcel;Deniz Gündüz;Slava Voloshynovskiy
In today’s digitally interconnected landscape, confirming the genuine associations between entities—whether they are items, devices, or individuals—and specific groups is critical. This paper introduces a new group membership verification method while ensuring minimal information loss, coupled with privacy-preservation and discrimination priors. Instead of verifying based on a similarity score in ...Show More
Template Inversion Attack Using Synthetic Face Images Against Real Face Recognition Systems
In this paper, we use synthetic data and propose a new method for template inversion attacks against face recognition systems. We use synthetic data to train a face reconstruction model to generate high-resolution (i.e., $1024\times 1024$ ) face images from facial templates. To this end, we use a face generator network to generate synthetic face images and extract their facial templates using the...Show More
Mitigating Demographic Bias in Face Recognition via Regularized Score Calibration
2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)
Year: 2024 | Conference Paper |
Cited by: Papers (3)
Demographic bias in deep learning-based face recog-nition systems has led to serious concerns. Several ex-isting works attempt to mitigate bias by incorporating demographic-specific processing during inference, which requires knowledge or learning of demographic attribute with an additional cost. We propose to regularize training of the face recognition CNN, for demographic fairness, by im-posing ...Show More
Vulnerability of State-of-the-Art Face Recognition Models to Template Inversion Attack
Hatef Otroshi Shahreza;Vedrana Krivokuća Hahn;Sébastien Marcel
Face recognition systems use the templates (extracted from users’ face images) stored in the system’s database for recognition. In a template inversion attack, the adversary gains access to the stored templates and tries to enter the system using images reconstructed from those templates. In this paper, we propose a framework to evaluate the vulnerability of face recognition systems to template in...Show More
Heterogeneous Face Recognition Using Domain Invariant Units
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2024 | Conference Paper |
Cited by: Papers (4)
Heterogeneous Face Recognition (HFR) aims to expand the applicability of Face Recognition (FR) systems to challenging scenarios, enabling the matching of face images across different domains, such as matching thermal images to visible spectra. However, the development of HFR systems is challenging because of the significant domain gap between modalities and the lack of availability of large-scale ...Show More
Vulnerability of Face age Verification to Replay Attacks
Pavel Korshunov;Anjith George;Gökhan Özbulak;Sébastien Marcel
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2024 | Conference Paper |
Presentation attacks on biometric systems have long created significant security risks. The increase in the adoption of age verification systems, which ensure that only age-appropriate content is consumed online, raises the question of vulnerability of such systems to replay presentation attacks. In this paper, we analyze the vulnerability of face age verification to simple replay attacks and asse...Show More
From Modalities to Styles: Rethinking the Domain Gap in Heterogeneous Face Recognition
Heterogeneous Face Recognition (HFR) focuses on matching faces from different domains, for instance, thermal to visible images, making Face Recognition (FR) systems more versatile for challenging scenarios. However, the domain gap between these domains and the limited large-scale datasets in the target HFR modalities make it challenging to develop robust HFR models from scratch. In our work, we vi...Show More
EdgeFace: Efficient Face Recognition Model for Edge Devices
Anjith George;Christophe Ecabert;Hatef Otroshi Shahreza;Ketan Kotwal;Sébastien Marcel
In this paper, we present EdgeFace - a lightweight and efficient face recognition network inspired by the hybrid architecture of EdgeNeXt. By effectively combining the strengths of both CNN and Transformer models, and a low rank linear layer, EdgeFace achieves excellent face recognition performance optimized for edge devices. The proposed EdgeFace network not only maintains low computational costs...Show More
Can personalised hygienic masks be used to attack face recognition systems?
Alain Komaty;Vedrana Krivokuća Hahn;Christophe Ecabert;Sébastien Marcel
2023 IEEE International Joint Conference on Biometrics (IJCB)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
The proliferation of automated face recognition (FR) necessitates increasingly accurate person identification. The COVID-19 pandemic has exposed the limitations of FR systems when presented with faces occluded by hygienic masks. However, the security risks of personalised hygienic mask attacks, whereby an attacker wears the mask on which the bottom part of an enrolled user’s face is printed, have ...Show More
Vulnerability of Automatic Identity Recognition to Audio-Visual Deepfakes
Pavel Korshunov;Haolin Chen;Philip N. Garner;Sébastien Marcel
2023 IEEE International Joint Conference on Biometrics (IJCB)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
The task of deepfakes detection is far from being solved by speech or vision researchers. Several publicly available databases of fake synthetic video and speech were built to aid the development of detection methods. However, existing databases typically focus on visual or voice modalities and provide no proof that their deepfakes can in fact impersonate any real person. In this paper, we present...Show More
Bridging the Gap: Heterogeneous Face Recognition with Conditional Adaptive Instance Modulation
2023 IEEE International Joint Conference on Biometrics (IJCB)
Year: 2023 | Conference Paper |
Cited by: Papers (4)
Heterogeneous Face Recognition (HFR) aims to match face images across different domains, such as thermal and visible spectra, expanding the applicability of Face Recognition (FR) systems to challenging scenarios. However, the domain gap and limited availability of large-scale datasets in the target domain make training robust and invariant HFR models from scratch difficult. In this work, we treat ...Show More
SynthDistill: Face Recognition with Knowledge Distillation from Synthetic Data
2023 IEEE International Joint Conference on Biometrics (IJCB)
Year: 2023 | Conference Paper |
Cited by: Papers (5)
State-of-the-art face recognition networks are often computationally expensive and cannot be used for mobile applications. Training lightweight face recognition models also requires large identity-labeled datasets. Meanwhile, there are privacy and ethical concerns with collecting and using large face recognition datasets. While generating synthetic datasets for training face recognition models is ...Show More
Approximating Optimal Morphing Attacks using Template Inversion
2023 IEEE International Joint Conference on Biometrics (IJCB)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Recent works have demonstrated the feasibility of inverting face recognition systems, enabling to recover convincing face images using only their embeddings. We leverage such template inversion models to develop a novel type of deep morphing attack based on inverting a theoretical optimal morph embedding, which is obtained as an average of the face embeddings of source images. We experiment with t...Show More
Inversion of Deep Facial Templates using Synthetic Data
2023 IEEE International Joint Conference on Biometrics (IJCB)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
In this paper, we use synthetic data and propose a new method to reconstruct high-resolution face images from facial templates in a template inversion attack against face recognition systems. We use a pre-trained face generator network to generate synthetic face images, and then learn a mapping from the facial templates to the intermediate latent space of the face generator network. We train our m...Show More
Template Inversion Attack against Face Recognition Systems using 3D Face Reconstruction
2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (4)
Face recognition systems are increasingly being used in different applications. In such systems, some features (also known as embeddings or templates) are extracted from each face image. Then, the extracted templates are stored in the system’s database during the enrollment stage and are later used for recognition. In this paper, we focus on template inversion attacks against face recognition syst...Show More
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