Facial Expressions as a Vulnerability in Face Recognition
This work identifies a vulnerability in face recognition systems due to facial expression bias, which is important for developers and users of these systems to address for improved security and reliability.
This paper investigates facial expression bias as a security vulnerability in face recognition systems, analyzing its presence in popular databases and its impact on state-of-the-art algorithms. The study found significant facial expression bias in widely used databases and a corresponding impact on the performance of face recognition algorithms.
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.