This work explores facial expression bias as a security vulnerability of face recognition systems. Despite the great performance achieved by state-of-the-art face recognition systems, the algorithms are still sensitive to a large range of covariates. We present a comprehensive analysis of how facial expression bias impacts the performance of face recognition technologies. Our study analyzes: i) facial expression biases in the most popular face recognition databases; and ii) the impact of facial expression in face recognition performances. Our experimental framework includes two face detectors, three face recognition models, and three different databases. Our results demonstrate a huge facial expression bias in the most widely used databases, as well as a related impact of face expression in the performance of state-of-the-art algorithms. This work opens the door to new research lines focused on mitigating the observed vulnerability.
@article{arxiv.2011.08809,
title = {Facial Expressions as a Vulnerability in Face Recognition},
author = {Alejandro Peña and Ignacio Serna and Aythami Morales and Julian Fierrez and Agata Lapedriza},
journal= {arXiv preprint arXiv:2011.08809},
year = {2021}
}
Comments
Proc. of IEEE Int. Conf. on Image Processing (ICIP)