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：如今，人脸识别系统在多个数据集上已超越人类表现。然而，仍存在机器无法正确分类的边缘案例。本文研究了人脸验证任务中机器与人工操作员组合的效果。首先，我们深入分析了多个最先进模型的边缘案例，以发现常见数据集中的挑战性场景。随后，我们针对这些选定任务开展了包含60名参与者的人类研究，并提供了详尽分析。最后，我们证明融合机器与人类决策可进一步提升多种基准数据集上最先进人脸验证系统的性能。相关代码与数据已在GitHub上公开。