Evidence map›Paper›PMID 40083764›Full record

ArticleFrontiers in psychology2025

Challenges of face identification with varied mask coverage in the post COVID-19 era.

Yi-Lang Chen, Shu-Yu Wang

Abstract read
In one paragraph

Article in Frontiers in psychology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Yi-Lang ChenDepartment of Industrial Engineering and Management, Ming Chi University of Technology, New Taipei, Taiwan.
Shu-Yu WangDepartment of Industrial Engineering and Management, Ming Chi University of Technology, New Taipei, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Recent studies have shown that wearing masks can influence face recognition abilities. During the COVID-19 pandemic, people became increasingly familiar with seeing masked faces, leading to a reduced familiarity with fully uncovered faces. With Taiwan now transitioning to a post-COVID-19 phase and the removal of mask mandates, this study investigates how varying levels of mask coverage affect face identification accuracy and response times. Methods: We examined three levels of mask coverage-full coverage (FC), coverage up to the middle of the nose bridge (MB), and coverage up to the bottom of the nose bridge (BB)-to determine their effects on identification performance. A computer-based simulation was conducted with 100 university students (50 men and 50 women), where participants completed 30 trials (5 trials for each mask coverage level across two target sexes). Each trial presented a masked target face corresponding to one of the three coverage levels, alongside four full-face images. Participants were instructed to choose the image that best matched the masked target face, with an option to select "None" if no match was found. Results: The findings indicate that faces with FC were identified both faster and more accurately, while those with MB coverage were the most challenging and time-consuming to recognize, particularly for female targets. The performance with BB coverage was intermediate between the other two levels. Conclusion: This study highlights a notable shift in face identification processes in the aftermath of the pandemic, with FC now leading to quicker and more accurate recognitions, suggesting a significant adaptability in human perceptual mechanisms. These results emphasize the importance of further research into face recognition as we continue to adapt to the pandemic's lasting effects on social interactions and identity verification.

Indexed as

accuracyface identificationmask coveragepost COVID-19 eratarget sex

Identifiers

PMID40083764
PMCPMC11905992

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.