Nowadays, face recognition systems surpass human performance on several datasets. However, there are still edge cases that the machine can't correctly classify. This paper investigates the effect of a combination of machine and human operators in the face verification task. First, we look closer at the edge cases for several state-of-the-art models to discover common datasets' challenging settings. Then, we conduct a study with 60 participants on these selected tasks with humans and provide an extensive analysis. Finally, we demonstrate that combining machine and human decisions can further improve the performance of state-of-the-art face verification systems on various benchmark datasets. Code and data are publicly available on GitHub.

翻译：现在，人脸识别系统已经能够在几个数据集上超越人类表现。然而，仍有一些边缘案例，机器无法正确分类。本文研究了将机器和人操作员的组合效应用于人脸验证任务。首先，我们更加深入地研究了现有先进模型在几个具体数据集上的边缘案例。然后，我们进行了一个由 60 个参与者完成的现实任务研究，并提供了详细的分析报告。最后，我们证明了将机器和人类决策相结合可以进一步提高先进的人脸验证系统在各种基准数据集上的性能。代码和数据已经公开在 GitHub 上。