Literature DB >> 24500751

Infrequent identity mismatches are frequently undetected.

Megan H Papesh1, Stephen D Goldinger.   

Abstract

The ability to quickly and accurately match faces to photographs bears critically on many domains, from controlling purchase of age-restricted goods to law enforcement and airport security. Despite its pervasiveness and importance, research has shown that face matching is surprisingly error prone. The majority of face-matching research is conducted under idealized conditions (e.g., using photographs of individuals taken on the same day) and with equal proportions of match and mismatch trials, a rate that is likely not observed in everyday face matching. In four experiments, we presented observers with photographs of faces taken an average of 1.5 years apart and tested whether face-matching performance is affected by the prevalence of identity mismatches, comparing conditions of low (10 %) and high (50 %) mismatch prevalence. Like the low-prevalence effect in visual search, we observed inflated miss rates under low-prevalence conditions. This effect persisted when participants were allowed to correct their initial responses (Experiment 2), when they had to verify every decision with a certainty judgment (Experiment 3) and when they were permitted "second looks" at face pairs (Experiment 4). These results suggest that, under realistic viewing conditions, the low-prevalence effect in face matching is a large, persistent source of errors.

Entities:  

Mesh:

Year:  2014        PMID: 24500751      PMCID: PMC4241395          DOI: 10.3758/s13414-014-0630-6

Source DB:  PubMed          Journal:  Atten Percept Psychophys        ISSN: 1943-3921            Impact factor:   2.199


  25 in total

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Authors:  Gary L Wells; Elizabeth A Olson
Journal:  Annu Rev Psychol       Date:  2002-06-10       Impact factor: 24.137

2.  Unfamiliar faces are not faces: evidence from a matching task.

Authors:  Ahmed M Megreya; A Mike Burton
Journal:  Mem Cognit       Date:  2006-06

3.  Low target prevalence is a stubborn source of errors in visual search tasks.

Authors:  Jeremy M Wolfe; Todd S Horowitz; Michael J Van Wert; Naomi M Kenner; Skyler S Place; Nour Kibbi
Journal:  J Exp Psychol Gen       Date:  2007-11

4.  Me, myself, and I: different recognition rates for three photo-IDs of the same person.

Authors:  Markus Bindemann; Adam Sandford
Journal:  Perception       Date:  2011       Impact factor: 1.490

5.  Exploring the time course of face matching: temporal constraints impair unfamiliar face identification under temporally unconstrained viewing.

Authors:  Müge Ozbek; Markus Bindemann
Journal:  Vision Res       Date:  2011-08-16       Impact factor: 1.886

6.  Matching identities of familiar and unfamiliar faces caught on CCTV images.

Authors:  V Bruce; Z Henderson; C Newman; A M Burton
Journal:  J Exp Psychol Appl       Date:  2001-09

7.  Exploring levels of face familiarity by using an indirect face-matching measure.

Authors:  Ruth Clutterbuck; Robert A Johnston
Journal:  Perception       Date:  2002       Impact factor: 1.490

8.  Varying target prevalence reveals two dissociable decision criteria in visual search.

Authors:  Jeremy M Wolfe; Michael J Van Wert
Journal:  Curr Biol       Date:  2010-01-14       Impact factor: 10.834

9.  Modeling confidence and response time in recognition memory.

Authors:  Roger Ratcliff; Jeffrey J Starns
Journal:  Psychol Rev       Date:  2009-01       Impact factor: 8.934

10.  Even in correctable search, some types of rare targets are frequently missed.

Authors:  Michael J Van Wert; Todd S Horowitz; Jeremy M Wolfe
Journal:  Atten Percept Psychophys       Date:  2009-04       Impact factor: 2.199

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  10 in total

1.  The confirmation and prevalence biases in visual search reflect separate underlying processes.

Authors:  Stephen C Walenchok; Stephen D Goldinger; Michael C Hout
Journal:  J Exp Psychol Hum Percept Perform       Date:  2020-03       Impact factor: 3.332

2.  The poverty of embodied cognition.

Authors:  Stephen D Goldinger; Megan H Papesh; Anthony S Barnhart; Whitney A Hansen; Michael C Hout
Journal:  Psychon Bull Rev       Date:  2016-08

3.  Error Rates in Users of Automatic Face Recognition Software.

Authors:  David White; James D Dunn; Alexandra C Schmid; Richard I Kemp
Journal:  PLoS One       Date:  2015-10-14       Impact factor: 3.240

4.  Effects of time pressure and time passage on face-matching accuracy.

Authors:  Matthew C Fysh; Markus Bindemann
Journal:  R Soc Open Sci       Date:  2017-06-07       Impact factor: 2.963

5.  Human-Computer Interaction in Face Matching.

Authors:  Matthew C Fysh; Markus Bindemann
Journal:  Cogn Sci       Date:  2018-06-28

6.  You shall not pass: how facial variability and feedback affect the detection of low-prevalence fake IDs.

Authors:  Dawn R Weatherford; William Blake Erickson; Jasmyne Thomas; Mary E Walker; Barret Schein
Journal:  Cogn Res Princ Implic       Date:  2020-01-28

7.  When experience does not promote expertise: security professionals fail to detect low prevalence fake IDs.

Authors:  Dawn R Weatherford; Devin Roberson; William Blake Erickson
Journal:  Cogn Res Princ Implic       Date:  2021-04-01

8.  The low prevalence effect in fingerprint comparison amongst forensic science trainees and novices.

Authors:  Bethany Growns; James D Dunn; Rebecca K Helm; Alice Towler; Jeff Kukucka
Journal:  PLoS One       Date:  2022-08-11       Impact factor: 3.752

9.  Matching Faces Against the Clock.

Authors:  Markus Bindemann; Matthew Fysh; Katie Cross; Rebecca Watts
Journal:  Iperception       Date:  2016-10-03

10.  Photo ID verification remains challenging despite years of practice.

Authors:  Megan H Papesh
Journal:  Cogn Res Princ Implic       Date:  2018-06-27
  10 in total

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