论文标题
面部表情是面部识别的脆弱性
Facial Expressions as a Vulnerability in Face Recognition
论文作者
论文摘要
这项工作探讨了面部表达偏见，这是面部识别系统的安全脆弱性。尽管最先进的面部识别系统取得了出色的性能，但该算法仍然对各种协变量敏感。我们对面部表达偏见如何影响面部识别技术的性能进行了全面分析。我们的研究分析：i）面部表达偏见在最受欢迎的面部识别数据库中； ii）面部表达在面部识别性能中的影响。我们的实验框架包括两个面部探测器，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存在巨大的面部表达偏见，以及面部表达在最新算法的性能中的相关影响。这项工作为新的研究线打开了大门，重点是减轻观察到的脆弱性。
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.
