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的表现已超越人类。然而，仍存在机器无法正确分类的边缘案例。本文研究了在人脸验证任务中结合机器与人工操作的效应。首先，我们深入审视了多个最先进模型所面临的边缘案例，以发现常见数据集中具有挑战性的场景。随后，我们选取了这些特定任务，组织了60名参与者开展研究并进行广泛分析。最后，我们证明，将机器决策与人类决策相结合，能在多个基准数据集上进一步提升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。