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：当前，人脸识别系统在多个数据集上的表现已超越人类水平。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者协同作用的效果。首先，我们深入剖析多个前沿模型在边缘案例中的表现，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一项包含60名参与者的人类研究，并进行了详尽分析。最后，我们证明将机器决策与人类决策相结合，能够进一步提升人脸验证系统在多个基准数据集上的性能。相关代码与数据已在GitHub上公开。