Evidence map›Paper›PMID 35347475›Full record

ArticleCognitive research: principles and implications2022

Improving face identification of mask-wearing individuals.

Krista D Manley, Jason C K Chan, Gary L Wells

Open access · goldAbstract read
In one paragraph

Article in Cognitive research: principles and implications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.4field-weighted citation impact, top 21% of its field
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

3 citing papers in PubMed, 13 citations in OpenAlex.

  1. Masked and elusive: congruency fails in video-to-photo face matching.Cognitive research: principles and implications · 2026
    Article
  2. Article
  3. The effect of surgical masks on identification decisions from masked and unmasked lineups.Psychiatry, psychology, and law : an interdisciplinary journal of the Australian and New Zealand Association of Psychiatry, Psychology and Law · 2024
    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

3 authors at 1 institution in 1 country.

Krista D ManleyIowa State University, Ames, USA. kristamanley26@gmail.com.ORCID 0000-0002-9678-6110
Jason C K ChanIowa State University, Ames, USA. ckchan@iastate.edu.
Gary L WellsIowa State University, Ames, USA.
Iowa State University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Research has consistently shown that concealing facial features can hinder subsequent identification. The widespread adoption of face masks due to the COVID-19 pandemic has highlighted the critical and urgent need to discover techniques to improve identification of people wearing face coverings. Despite years of research on face recognition and eyewitness identifications, there are currently no evidence-based recommendations for lineup construction for cases involving masked individuals. The purpose of this study was to examine identification accuracy of a masked perpetrator as a function of lineup type (i.e., unmasked or masked lineups) and perpetrator presence (i.e., absent or present). In both experiments, discriminability was superior for masked lineups, a result that was due almost exclusively to higher hits rates in target-present conditions. These data suggest that presenting a masked lineup can enhance identification of masked faces, and they have important implications for both eyewitness identification and everyday face recognition of people with face coverings.

Indexed as

COVID-19Facial RecognitionHumansPandemicsRecognition, PsychologyEyewitness identificationFace coveringFace recognitionLineupsMask

Identifiers

PMID35347475
PMCPMC8960223
OpenAlexW4220716960

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.