Benchmarking Multimodal Large Language Models for Face Recognition
Multimodal large language models (MLLMs) have achieved remarkable performance across diverse vision-and-language tasks. However, their potential in face recognition remains …
Multimodal large language models (MLLMs) have achieved remarkable performance across diverse vision-and-language tasks. However, their potential in face recognition remains …
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 …
Cross-spectral face recognition systems are designed to enhance the performance of facial recognition systems by enabling cross-modal matching under challenging operational …
This review consolidates research on demographic fairness in face recognition, covering causes, datasets, evaluation metrics, mitigation approaches, and open challenges for …
Face Recognition (FR) 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 …
Face recognition datasets are often collected by crawling Internet and without individuals' consents, raising ethical and privacy concerns. Generating synthetic datasets for …
The increasing reliance on large-scale datasets in machine learning poses significant privacy and ethical challenges, particularly in sensitive domains such as face recognition. …
The accuracy of face recognition systems has improved significantly in the past few years, thanks to the large amount of data collected and the advancement in neural network …
Privacy-preserving biometric technologies for passenger identification and verification at EU external borders.