Abstract Facial recognition systems are increasingly popular and prevalent in our everyday lives, especially on mobile cell phones. This paper is an attempt to investigate the effects that makeup and facial expressions have on the reliability of such systems. While these factors have been shown not to be significant by themselves, it has not been clearly demonstrated whether a combination of both these factors can affect the matching accuracy of the same system in a statistically meaningful way. We carried out numerical experiments through two databases: Radboud Faces database and Psychological Image Collection at Stirling (PICS), using a state of the art algorithm, namely dlib. Then, in order to be able to reliably validate the results, we used two more algorithms (Verilook and VGGFace) to give similarity scores. The results showed that, while the effects of makeup and varied mood expressions are not significant by themselves, the joint effect is. An equal error rate (EER) of 4.68% was achieved when identifying faces under the joint influences of full makeup and mood variation, while the EER under the effect of each of these factors separately is less than 1%.

中文翻译：

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情绪变化和化妆同时影响下人脸识别系统的可靠性研究

摘要 面部识别系统在我们的日常生活中越来越流行，尤其是在手机上。本文试图研究化妆和面部表情对此类系统可靠性的影响。虽然这些因素本身并不显着，但尚未明确证明这两个因素的组合是否能以统计上有意义的方式影响同一系统的匹配精度。我们通过两个数据库进行了数值实验：Radboud Faces 数据库和斯特灵心理图像收集 (PICS)，使用最先进的算法，即 dlib。然后，为了能够可靠地验证结果，我们使用了另外两种算法（Verilook 和 VGGFace）来给出相似度分数。结果表明，虽然化妆和各种情绪表达的影响本身并不显着，但联合效应却很明显。在全妆和情绪变化的共同影响下识别人脸时实现了 4.68% 的等错误率 (EER)，而在这些因素分别影响下的 EER 小于 1%。