Crowd Effects in Unfamiliar Face Matching

White, David; Burton, A. Mike; Kemp, Richard I.; Jenkins, Rob

doi:10.1002/acp.2971

Cited by 47 publications

(84 citation statements)

References 30 publications

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“…Analogous results have been reported by White et al . (), who show that aggregating responses over large groups can also significantly improve accuracy on such face matching tasks. The results presented here complement the White et al .…”

Section: Discussionmentioning

confidence: 99%

Unfamiliar face matching: Pairs out‐perform individuals and provide a route to training

Dowsett

Burton

2014

British J of Psychology

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134

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Matching unfamiliar faces is known to be difficult. Here, we ask whether performance can be improved by asking viewers to work in pairs, a manipulation known to increase accuracy for low-level visual discrimination tasks. Across four experiments we consistently find that face matching accuracy is higher for pairs of viewers than for individuals. This 'pairs advantage' is generally driven by adopting the response of the higher scoring partner. However, when the task becomes difficult, both partners' performance is improved by working in a pair. In two experiments, we find evidence that working in a pair can lead to subsequent improvements in individual performance, specifically for viewers whose accuracy is initially low. The pairs' technique therefore offers the opportunity for substantial improvements in face matching performance, along with an added training benefit.

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Section: Discussionmentioning

confidence: 99%

Unfamiliar face matching: Pairs out‐perform individuals and provide a route to training

Dowsett

Burton

2014

British J of Psychology

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134

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“…To model the effectiveness of this process, we conducted simulations to 'fuse' or combine judgements at the level of individual image pairs. The simulations followed previous work showing that aggregating the judgements of multiple participants improves identification accuracy [26,27]. We focused on data from the 30 s upright EFCT as this experimental condition most closely resembles working practice of forensic examiners; however, we also carried out these simulations with the PICT and found comparable results.…”

Section: (D) Fusion Analysismentioning

confidence: 93%

Perceptual expertise in forensic facial image comparison

et al. 2015

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Forensic facial identification examiners are required to match the identity of faces in images that vary substantially, owing to changes in viewing conditions and in a person's appearance. These identifications affect the course and outcome of criminal investigations and convictions. Despite calls for research on sources of human error in forensic examination, existing scientific knowledge of face matching accuracy is based, almost exclusively, on people without formal training. Here, we administered three challenging face matching tests to a group of forensic examiners with many years' experience of comparing face images for law enforcement and government agencies. Examiners outperformed untrained participants and computer algorithms, thereby providing the first evidence that these examiners are experts at this task. Notably, computationally fusing responses of multiple experts produced near-perfect performance. Results also revealed qualitative differences between expert and non-expert performance. First, examiners' superiority was greatest at longer exposure durations, suggestive of more entailed comparison in forensic examiners. Second, experts were less impaired by image inversion than nonexpert students, contrasting with face memory studies that show larger face inversion effects in high performers. We conclude that expertise in matching identity across unfamiliar face images is supported by processes that differ qualitatively from those supporting memory for individual faces.

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“…Although research into this question is ongoing, few solutions have been found so far. In terms of the process, we know that working together in pairs (Dowsett & Burton, 2015) or aggregating the responses of groups of individuals (White, Burton, Kemp, & Jenkins, 2013) can increase accuracy. Regarding the materials, evidence suggests that using computer-generated averages or arrays of instances can improve performance over comparison with a single image (White, Burton, Jenkins, & Kemp, 2014).…”

Section: Introductionmentioning

confidence: 99%

Unfamiliar Face Matching With Frontal and Profile Views

Kramer

Reynolds

2018

Perception

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Research has systematically examined how laboratory participants and real-world practitioners decide whether two face photographs show the same person or not, using frontal images. In contrast, research has not examined face matching using profile images. In Experiment 1, we ask whether matching unfamiliar faces is easier with frontal compared with profile views. Participants completed the original, frontal version of the Glasgow Face Matching Test, and also an adapted version where all face pairs were presented in profile. There was no difference in performance across the two tasks, suggesting that both views were similarly useful for face matching. Experiments 2 and 3 examined whether matching unfamiliar faces is improved when both frontal and profile views are provided. We compared face matching accuracy when both a frontal and a profile image of each face was presented, with accuracy using each view alone. Surprisingly, we found no benefit when both views were presented together in either experiment. Overall, these results suggest that either frontal and profile views provide substantially overlapping information regarding identity or participants are unable to utilise both sources of information when making decisions. Each of these conclusions has important implications for face matching research and real-world identification development.

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Crowd Effects in Unfamiliar Face Matching

Cited by 47 publications

References 30 publications

Unfamiliar face matching: Pairs out‐perform individuals and provide a route to training

Unfamiliar face matching: Pairs out‐perform individuals and provide a route to training

Perceptual expertise in forensic facial image comparison

Unfamiliar Face Matching With Frontal and Profile Views

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