7 citations · 9 across the 9 of their papers we have counts for
9 papers
Detecting Text Manipulation in Images using Vision Language Models
Vidit Vidit, Pavel Korshunov, Amir Mohammadi +3
Recent works have shown the effectiveness of Large Vision Language Models (VLMs or LVLMs) in image manipulation detection. However, text manipulation detection is largely missing i…
FantasyID: A dataset for detecting digital manipulations of ID-documents
Pavel Korshunov, Amir Mohammadi, Vidit Vidit +2
Advancements in image generation led to the availability of easy-to-use tools for malicious actors to create forged images. These tools pose a serious threat to the widespread Know…
Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data
Pavel Korshunov, Ketan Kotwal, Christophe Ecabert +3
Synthetic data has emerged as a promising alternative for training face recognition (FR) models, offering advantages in scalability, privacy compliance, and potential for bias miti…
Score Normalization for Demographic Fairness in Face Recognition
Yu Linghu, Tiago de Freitas Pereira, Christophe Ecabert +2
Fair biometric algorithms have similar verification performance across different demographic groups given a single decision threshold. Unfortunately, for state-of-the-art face reco…
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…
FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Data
Pietro Melzi, Ruben Tolosana, Ruben Vera-Rodriguez +44
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…
Toward responsible face datasets: modeling the distribution of a disentangled latent space for sampling face images from demographic groups
Parsa Rahimi, Christophe Ecabert, Sebastien Marcel
Recently, it has been exposed that some modern facial recognition systems could discriminate specific demographic groups and may lead to unfair attention with respect to various fa…
EFaR 2023: Efficient Face Recognition Competition
Jan Niklas Kolf, Fadi Boutros, Jurek Elliesen +24
This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition rece…
EdgeFace: Efficient Face Recognition Model for Edge Devices
Anjith George, Christophe Ecabert, Hatef Otroshi Shahreza +2
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…