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arXiv:2103.01924 (cs)
COVID-19 e-print

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[Submitted on 2 Mar 2021 (v1), last revised 24 Apr 2022 (this version, v3)]

Title:Masked Face Recognition: Human vs. Machine

Authors:Naser Damer, Fadi Boutros, Marius Süßmilch, Meiling Fang, Florian Kirchbuchner, Arjan Kuijper
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Abstract: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 (FR) in a collaborative environment is a currently sensitive yet understudied issue. Recent reports have tackled this by evaluating the masked probe effect on the performance of automatic FR 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 FR solutions. This involves an extensive evaluation by 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 humans and machines.
Comments: Accepted at the IET Biometrics journal (2022)
Subjects: Computer Vision and Pattern Recognition (cs.CV); Computers and Society (cs.CY)
Cite as: arXiv:2103.01924 [cs.CV]
  (or arXiv:2103.01924v3 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2103.01924

Submission history

From: Naser Damer [view email]
[v1] Tue, 2 Mar 2021 18:36:01 UTC (3,147 KB)
[v2] Wed, 2 Jun 2021 16:41:01 UTC (3,061 KB)
[v3] Sun, 24 Apr 2022 17:17:41 UTC (3,151 KB)
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