  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.
