Facial Expressions as a Vulnerability in Face Recognition

TL;DR

This paper investigates how facial expression biases serve as a security vulnerability in face recognition systems, revealing significant biases in databases and their impact on recognition performance.

Contribution

It provides a comprehensive analysis of facial expression bias in popular databases and its effect on face recognition accuracy, highlighting a critical vulnerability.

Findings

Large facial expression bias in widely used databases

Facial expressions significantly affect recognition performance

Biases pose a security vulnerability in face recognition systems

Abstract

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.