24 citations · 80 across the 28 of their papers we have counts for
29 papers
Foundation and Multimodal Large Language Models for Face Presentation and Morph Attack Detection
Hatef Otroshi Shahreza, Asif Hussain Khan, Peter Lorenz +2
Face recognition systems are increasingly deployed in security-critical applications, yet they remain vulnerable to presentation and morph attacks. Presentation attack detection (P…
Improving Low-Resolution Face Recognition under Limited Data: How Synthetic Data Generation Can Close the Domain Gap
Luis S. Luevano, Ünsal Öztürk, Hatef Otroshi Shahreza +2
Face Recognition (FR) systems in surveillance settings often encounter Low Resolution (LR) faces, those whose face region falls below the standard 112 112 input size. Whil…
Foundation Models for Face Presentation Attack Detection: A Unified Linear-Probing Benchmark
Peter Lorenz, Anjith George, Sébastien Marcel
Face presentation attack detection (PAD) remains challenging under cross-dataset evaluation, where domain shift degrades models trained on a single dataset. The scarcity of large-s…
IJCB-AFMFR 2026: Competition on Adapting Foundation Models for Face Recognition Using Synthetic Training Data
Tahar Chettaoui, Guray Ozgur, Eduarda Caldeira +13
This paper presents a summary of the Competition on Adapting Foundation Models for Face Recognition Using Synthetic Training Data (AFMFR), held at the 2026 International Joint Conf…
Demographic Fairness in Multimodal LLMs: A Benchmark of Gender and Ethnicity Bias in Face Verification
Ünsal Öztürk, Hatef Otroshi Shahreza, Sébastien Marcel
Multimodal Large Language Models (MLLMs) have recently been explored as face verification systems that determine whether two face images are of the same person. Unlike dedicated fa…
Evaluating Multimodal Large Language Models for Heterogeneous Face Recognition
Hatef Otroshi Shahreza, Anjith George, Sébastien Marcel
Multimodal Large Language Models (MLLMs) have recently demonstrated strong performance on a wide range of vision-language tasks, raising interest in their potential use for biometr…
Benchmarking Multimodal Large Language Models for Face Recognition
Hatef Otroshi Shahreza, Sébastien Marcel
Multimodal large language models (MLLMs) have achieved remarkable performance across diverse vision-and-language tasks. However, their potential in face recognition remains underex…
ArtFace: Towards Historical Portrait Face Identification via Model Adaptation
Francois Poh, Anjith George, Sébastien Marcel
Identifying sitters in historical paintings is a key task for art historians, offering insight into their lives and how they chose to be seen. However, the process is often subject…
EdgeDoc: Hybrid CNN-Transformer Model for Accurate Forgery Detection and Localization in ID Documents
Anjith George, Sebastien Marcel
The widespread availability of tools for manipulating images and documents has made it increasingly easy to forge digital documents, posing a serious threat to Know Your Customer (…
FaceLLM: A Multimodal Large Language Model for Face Understanding
Hatef Otroshi Shahreza, Sébastien Marcel
Multimodal large language models (MLLMs) have shown remarkable performance in vision-language tasks. However, existing MLLMs are primarily trained on generic datasets, limiting the…
The Invisible Threat: Evaluating the Vulnerability of Cross-Spectral Face Recognition to Presentation Attacks
Anjith George, Sebastien Marcel
Cross-spectral face recognition systems are designed to enhance the performance of facial recognition systems by enabling cross-modal matching under challenging operational conditi…
xEdgeFace: Efficient Cross-Spectral Face Recognition for Edge Devices
Anjith George, Sebastien Marcel
Heterogeneous Face Recognition (HFR) addresses the challenge of matching face images across different sensing modalities, such as thermal to visible or near-infrared to visible, ex…
Exploring ChatGPT for Face Presentation Attack Detection in Zero and Few-Shot in-Context Learning
Alain Komaty, Hatef Otroshi Shahreza, Anjith George +1
This study highlights the potential of ChatGPT (specifically GPT-4o) as a competitive alternative for Face Presentation Attack Detection (PAD), outperforming several PAD models, in…
Second FRCSyn-onGoing: Winning Solutions and Post-Challenge Analysis to Improve Face Recognition with Synthetic Data
Ivan DeAndres-Tame, Ruben Tolosana, Pietro Melzi +56
Synthetic data is gaining increasing popularity for face recognition technologies, mainly due to the privacy concerns and challenges associated with obtaining real data, including…
Face Reconstruction from Face Embeddings using Adapter to a Face Foundation Model
Hatef Otroshi Shahreza, Anjith George, Sébastien Marcel
Face recognition systems extract embedding vectors from face images and use these embeddings to verify or identify individuals. Face reconstruction attack (also known as template i…
HyperFace: Generating Synthetic Face Recognition Datasets by Exploring Face Embedding Hypersphere
Hatef Otroshi Shahreza, Sébastien Marcel
Face recognition datasets are often collected by crawling Internet and without individuals' consents, raising ethical and privacy concerns. Generating synthetic datasets for traini…
Digi2Real: Bridging the Realism Gap in Synthetic Data Face Recognition via Foundation Models
Anjith George, Sebastien Marcel
The accuracy of face recognition systems has improved significantly in the past few years, thanks to the large amount of data collected and advancements in neural network architect…
Unveiling Synthetic Faces: How Synthetic Datasets Can Expose Real Identities
Hatef Otroshi Shahreza, Sébastien Marcel
Synthetic data generation is gaining increasing popularity in different computer vision applications. Existing state-of-the-art face recognition models are trained using large-scal…
Modality Agnostic Heterogeneous Face Recognition with Switch Style Modulators
Anjith George, Sebastien Marcel
Heterogeneous Face Recognition (HFR) systems aim to enhance the capability of face recognition in challenging cross-modal authentication scenarios. However, the significant domain…
Synthetic Face Datasets Generation via Latent Space Exploration from Brownian Identity Diffusion
David Geissbühler, Hatef Otroshi Shahreza, Sébastien Marcel
Face recognition models are trained on large-scale datasets, which have privacy and ethical concerns. Lately, the use of synthetic data to complement or replace genuine data for th…
Heterogeneous Face Recognition Using Domain Invariant Units
Anjith George, Sebastien Marcel
Heterogeneous Face Recognition (HFR) aims to expand the applicability of Face Recognition (FR) systems to challenging scenarios, enabling the matching of face images across differe…
From Modalities to Styles: Rethinking the Domain Gap in Heterogeneous Face Recognition
Anjith George, Sebastien Marcel
Heterogeneous Face Recognition (HFR) focuses on matching faces from different domains, for instance, thermal to visible images, making Face Recognition (FR) systems more versatile…
Second Edition FRCSyn Challenge at CVPR 2024: Face Recognition Challenge in the Era of Synthetic Data
Ivan DeAndres-Tame, Ruben Tolosana, Pietro Melzi +55
Synthetic data is gaining increasing relevance for training machine learning models. This is mainly motivated due to several factors such as the lack of real data and intra-class v…
SDFR: Synthetic Data for Face Recognition Competition
Hatef Otroshi Shahreza, Christophe Ecabert, Anjith George +25
Large-scale face recognition datasets are collected by crawling the Internet and without individuals' consent, raising legal, ethical, and privacy concerns. With the recent advance…