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.

翻译：这项工作探索面部表达偏差作为面部识别系统的一种安全弱点。 尽管最先进的面部识别系统取得了巨大绩效, 算法仍然对大量共变系统十分敏感。 我们全面分析了面部表达偏差如何影响面部识别技术的性能。 我们的研究分析:(1) 最受欢迎的面部识别数据库中的面部表达偏差;(2) 面部表达在面部识别性表现中的影响。 我们的实验框架包括两个面部检测器、三个面部识别模型和三个不同的数据库。 我们的结果表明,在最广泛使用的数据库中,面部表达偏差巨大,以及面部表现在最先进的算法的性表现中也产生了相关影响。 这项工作为侧重于减轻所观察到的脆弱性的新研究线打开了大门。