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:现今,人脸识别系统在几个数据集上已经超越了人类的表现。然而,仍有一些边缘用例机器无法正确分类。本文研究了一个机器与人操作员相结合的人脸验证任务的影响。首先,我们近距离观察了几个最新模型在边缘用例上的表现,以发现常见数据集的具有挑战性的设置。然后,我们进行了一个60个参与者的研究,对这些选择任务与人类的表现进行了广泛的分析。最后,我们证明,结合机器和人的决策可以进一步提高最先进的人脸验证系统在各种基准数据集上的性能。代码和数据公开在GitHub上。