  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.
