  The recent COVID-19 pandemic has increased the focus on hygienic and
contactless identity verification methods. However, the pandemic led to the
wide use of face masks, essential to keep the pandemic under control. The
effect of wearing a mask on face recognition in a collaborative environment is
currently sensitive yet understudied issue. Recent reports have tackled this by
evaluating the masked probe effect on the performance of automatic face
recognition solutions. However, such solutions can fail in certain processes,
leading to performing the verification task by a human expert. This work
provides a joint evaluation and in-depth analyses of the face verification
performance of human experts in comparison to state-of-the-art automatic face
recognition solutions. This involves an extensive evaluation with 12 human
experts and 4 automatic recognition solutions. The study concludes with a set
of take-home-messages on different aspects of the correlation between the
verification behavior of human and machine.
