Selecting police super-recognisers

James D Dunn; Alice Towler; Richard I Kemp; David White

doi:10.1371/journal.pone.0283682

. 2023 May 17;18(5):e0283682. doi: 10.1371/journal.pone.0283682

Selecting police super-recognisers

James D Dunn ¹, Alice Towler ^1,², Richard I Kemp ¹, David White ^1,^*

Editor: Rocco Palumbo³

PMCID: PMC10191310 PMID: 37195905

Abstract

People vary in their ability to recognise faces. These individual differences are consistent over time, heritable and associated with brain anatomy. This implies that face identity processing can be improved in applied settings by selecting high performers–‘super-recognisers’ (SRs)–but these selection processes are rarely available for scientific scrutiny. Here we report an ‘end-to-end’ selection process used to establish an SR ‘unit’ in a large police force. Australian police officers (n = 1600) completed 3 standardised face identification tests and we recruited 38 SRs from this cohort to complete 10 follow-up tests. As a group, SRs were 20% better than controls in lab-based tests of face memory and matching, and equalled or surpassed accuracy of forensic specialists that currently perform face identification tasks for police. Individually, SR accuracy was variable but this problem was mitigated by adopting strict selection criteria. SRs’ superior abilities transferred only partially to body identity decisions where the face was not visible, and they were no better than controls at deciding which visual scene that faces had initially been encountered in. Notwithstanding these important qualifications, we conclude that super-recognisers are an effective solution to improving face identity processing in applied settings.

1 Introduction

Identifying faces underpins everyday social interactions, and many important applied identity processing tasks. Staff working in forensic and security settings often perform face identity processing tasks in large volumes. For example, passport officers compare faces of travellers to their passports, CCTV operators monitor surveillance for persons of interest, and forensic facial examiners prepare image comparison reports for use by courts and in criminal investigations. Although facial recognition technology is increasingly used to reduce the need for human processing in many of these applied tasks, this technology requires human oversight by people with sufficient skill to detect errors [1].

Human errors in these tasks can have profound consequences for individuals and for society, for example wrongful convictions or terrorist attacks. But human errors are also surprisingly common, with experimental studies asking participants to decide if two images show the same person reporting error rates between 30–40% for challenging tasks [2,3] to 20% when conditions are optimised for high accuracy [4]. Similar error rates are reported in studies of professional groups that perform face image comparison tasks in their daily work: groups of bank tellers, security, passport and police officers all achieve equivalent accuracy to groups of university students [5].

A promising solution to this problem has emerged from the science of face perception. It is now firmly established that face identity processing ability varies between individuals to a surprisingly large extent. This ability ranges from people with prosopagnosia, who are unable to recognise even their closest friends and family members [6], to super-recognisers who consistently attain the highest levels of accuracy on tests of face identification ability [7]. These extremes form endpoints of a normally distributed cognitive trait that varies dimensionally in the broader population [8].

Several published tests are designed to measure face identity processing ability across the ability spectrum (see [8] for a recent review, see also [9]). The most widely used is the Cambridge Face Memory Test which measures the ability to learn and remember unfamiliar faces [10]. Research has established that a person’s CFMT score is stable over time [11], that there is a substantial genetic contribution to performance [11–13], and that variation in CFMT scores are associated with measures of brain structure and physiology (e.g. [14,15]). Similar levels of stability in individuals’ performance are found in face matching tests that do not involve memory but instead deciding whether images shown at the same time are of the same person [16]. There is also high correlation between tests of face identification with diverse task demands, pointing to a common, trait-like ability that generalises across identity processing tasks (e.g. [17,18]).

Practical outcomes have stemmed from this research. Police, government and private organisations have used face identification tests to select staff for specialist operational roles [19]. The idea of using tests of face identity processing to select teams of face identification specialists was first explored by the London Metropolitan Police [20,21], and subsequently by the Australian Passport Office [22], Queensland [23] and Berlin police forces [24,25]. Some of the groups resulting from these selection processes have been formally tested and show higher accuracy in small-scale studies. For example, London Metropolitan police SRs outperform control participants by 10 percentage points on average in [20] (n = 10) and 17 percentage points in [21] (n = 4).

However, it is typically unclear exactly how members of these super-recogniser teams were initially selected, and details of their professional experience and training are unknown (but see 25). Moreover, the full testing process leading to their recruitment has not yet been formally reported, and so gains in accuracy that are attainable through staff selection alone have not been quantified. One study estimated the gains of selecting a small cohort of undergraduate students ([26]; n = 114), and found that selecting the top 10 performers could produce accuracy gains of 8%. Comparable accuracy differences between super-recognisers selected from the general public and standard comparison groups have been found in other studies, ranging from 8% to 20% (e.g. [27–29]). Other studies have compared super-recognisers selected from the general population to existing professional cohorts of forensic facial examiners and found equivalent accuracy [3,23,30]. This work points to the potential for super-recognisers to improve accuracy in forensic settings. Importantly however, these studies do not evaluate end-to-end procedures for selecting super-recognisers in organisations.

Here we provide a detailed report of the processes used to select super-recognisers in the New South Wales Police Force, a large Australian organisation with approximately 15,000 police officers and 4,000 civilian staff. Our main aim was to provide a data-driven roadmap for selecting super-recognisers on the basis of face identity processing tests, quantifying the potential benefit of adopting this approach for similar organisations.

To do this, we first used online tests to identify individuals who met specific performance criteria on three standardised tests of unfamiliar face memory and face matching ability. Because there is no universally agreed criterion for selecting super-recognisers (see [9], c.f. [31]) we applied two alternative selection criteria and assessed the impact of these criteria on group performance in follow-up tests in our research lab. Follow-up testing included challenging face matching tests used in prior work, to benchmark their accuracy against high performing professional groups of forensic facial examiners [3,22,23,32]. Forensic facial examiners are the most commonly used face identification experts in forensic and security settings, providing a useful standard for assessing super-recognisers potential value. We also designed bespoke tests that captured a wider range of task demands encountered in police work and compared super-recogniser performance on these to standard participant groups.

To pre-empt our results, we find comparable accuracy between super-recognisers and existing professional experts on a range of unfamiliar face matching tasks that are representative of police work. Indeed, super-recognisers were far more accurate than these comparison groups when limited to study the faces to just two seconds. Face inversion effects in super-recognisers and facial examiners also point to qualitative differences in the nature of their face identity processing.

When comparing to standard participant groups of university students, we find that super-recognisers had consistently higher accuracy on challenging applied tasks (Method & Results section 1). Summary analysis shows that this superiority was notably larger when super-recognisers were selected by a strict versus weak selection criteria (see Method & Results section 3). More detailed cognitive testing shows that super-recognisers also have a heightened ability to perform perceptual comparison tasks on non-face stimuli, but these abilities appear to be graded by the extent to which the perceptual information is person-related (Face > Body > Houses; see Method & Results section 2). We also show an important limitation of super-recognisers’ face recognition abilities: While they were far better at recognising unfamiliar faces, they were no better at remembering the context in which they initially encountered the face (Method & Results section 2).

2 Method and results

The data that support the findings of this study are openly available on the Open Science Framework (https://doi.org/10.17605/OSF.IO/BJDWK).

Super-recognisers (n = 38) were selected from a cohort of 1600 New South Wales Police Force employees that completed 3 screening tests on their workplace computers. Screening tests were three standardised tests of face matching and memory administered online: the Glasgow Face Matching Test (GFMT; [4]); the Cambridge Face Memory Test long form (CFMT+; [7]), and the UNSW Face Test [33]. Average performance of the 1600 police officers was consistent with normative data from standard participant groups, except they exceeded norms on the GFMT (see Table 1 in Methods).

Table 1. Comparison of NSW Police Force staff to normative data on 3 screening tests.

See screening tests section above for sources of normative data.

	Normative		NSW Police
	Mean	SD	Mean	SD	N
CFMT+	69.6	11.5	70.0	11.7	2019
GFMT	81.3	9.7	89.5	8.2	2465
UNSW	58.9	5.8	59.7	8.0	1742

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We applied two selection criteria to scores on these screening tests. This produced two overlapping groups of participants that we invited for further cognitive testing in our lab. “Strict SR” criteria participants achieved scores that exceeded 1.7 standard deviations above the published normative mean on all three screening tests (SR-Strict, n = 15 identified; n = 11 participated). “Weak SR” criteria participants, which included the participants in the strict criteria group, had an average score across the three screening tests that exceeded 1.7 standard deviations above the published normative mean (SR-Weak, n = 54 identified; n = 38 participated). Full details of the method for selecting super-recognisers are provided in Methods (see Participants section).

Follow-up cognitive testing in our lab had four main aims. First, it was important to ensure that individuals selected as super-recognisers could demonstrate sustained levels of high accuracy on a series of challenging face identification tasks with varying task demands many weeks after the initial screening tests. Second, it enabled us to compare the face identification performance of groups selected by strict versus weak criteria, to quantify the benefit of selecting face identification specialists in applied settings (summary analysis comparing these groups across all tests is given in the final Method and Results section). Third, we tested for quantitative and qualitative differences in super-recogniser abilities relative to professional experts in forensic face identification who are routinely employed in police forces. Fourth, we tested the boundary conditions of super-recognisers’ abilities on tasks that do not involve face processing but are nevertheless important in police work.

2.1. Comparing police super-recognisers to facial examiners on challenging face identification tests in the lab verifies their superior ability

Next, we tested super-recognisers’ ability to perceptually match face identity on challenging tasks routinely performed in forensic settings. These tests were challenging because images of the same face used in matching test items were taken at different times, often years apart, in different environmental conditions and using different cameras. For items where images were of different faces, nonmatching ‘foil’ images were selected as similar to target faces using facial recognition technology. In addition to making the task more challenging, selecting images in this way reflects many modern applied tasks in forensic police work where officers often use facial recognition technology to assist with investigations (e.g., [22]). Because all of these tests have been used in prior work to test facial forensic examiners, it enabled us to benchmark accuracy of super-recognisers to an established industry standard. Examples of test items from the facial recognition candidate list test [22] and the selfie-to-passport test and shown in Fig 1 and further details of these tests are provided in the Methods.

2.1.1 Facial recognition candidate list test

As shown in Fig 1, both groups of super-recognisers outperformed Controls, and were as accurate as Facial Examiners on this task (SR-Weak vs. Control: t(77.4) = 6.77, p < .001, Cohen’s d = 1.49; SR-Strict vs. Control: t(33.8) = 10.60, p < .001, Cohen’s d = 2.33; SR-Weak vs. Examiners: t(7.4) = 0.55, p = .597, Cohen’s d = 0.27; SR-Strict vs. Examiners: t(6.9) = 0.64, p = .541, Cohen’s d = 0.38).

We also found that the SR-Strict group (M = 73.4%, SD = 5.9%) was numerically more accurate than the SR-Weak group (M = 65.4%, SD = 13.1%) (Mean difference = 8.0%, Cohen’s d = 0.67). The fact that the SR-strict group were a subset of the SR-weak group makes inferential tests problematic, but the difference in accuracy between these groups is important in applied settings to guide policy decisions. Therefore, in each section that follows we present effect size in terms of percentage points and test the consistency of this difference across all tests reported below in the final section of Methods and Results (see Summary performance profiles show benefits of stricter criteria when selecting groups of super-recognisers).

2.1.2 Selfie-to-passport test

We found equivalent accuracy in the SR-Strict group and Facial Examiners but the SR-Weak group was significantly less accurate than Facial Examiners (SR-Weak vs. Examiners: t(35.8) = -3.55, p = .001, Cohen’s d = -0.63; SR-Strict vs. Examiners: t(11.0) = 1.14, p = .280, Cohen’s d = 0.39). Both the SR-Weak and SR-Strict groups were more accurate than Controls (SR-Weak vs. Controls: t(65.9) = 8.08, p < .001, Cohen’s d = 1.93; SR-Strict vs. Controls: t (22.9) = 7.59, p < .001, Cohen’s d = 2.29). There was a small numerical advantage for the SR-Strict (M = 0.82, SD = 0.06) over the SR-Weak group (M = 0.80, SD = 0.08; Mean difference = 0.03, Cohen’s d = 0.34).

2.1.3 Qualitative differences between super-recognisers and facial examiners in face matching

Prior work has shown qualitative differences in the way that facial examiners perform face matching tasks relative to student comparison groups. In the Expertise in Facial Comparison Test (EFCT, [32]; see Fig 2), examiners’ superiority over controls was only evident when given sufficient time to study images, and not when asked to make quick judgments (see also [23]). Facial examiners also showed less impairment as a result of viewing the faces upside down, suggesting more feature-based comparison strategy to the task relative to typical viewers (see also [34]). However, unlike facial examiners–whose face identification abilities are the product of training and experience (see [5,35])–super-recognisers’ abilities stem from natural variation in individual ability, and so we might expect differences in their perceptual processing. Here we compared these qualitative patterns in super-recognisers to facial examiners using the EFCT (see Fig 2, top; see Methods for further details).

Fig 2 — Participants decided whether two images, taken months apart and in different environmental conditions, showed the same person or two different people (top). Super-recognisers were more accurate than examiners when making decisions under a short time limit (2 seconds, top left panel) but not when participants were given longer to study the images (30 seconds, top right). Qualitative differences between groups were also observed in the effect of turning faces upside down (Inversion effect, bottom panels: See text for details).

We compared qualitative patterns of performance on the EFCT with two separate 2-way ANOVAs described below. First, comparing accuracy on upright faces for groups in 2s Vs 30s study duration. Second, comparing the size of Face Inversion Effects in 2s Vs 30s study duration. We also conducted a 4 x 2 x 2 mixed factors ANOVA with Group as the between-subjects factor and Study Duration (2s, 30s) and Face Orientation (upright, inverted) as the within-subjects factors. The full three-factor analysis is reported in S1 Appendix.

Study duration. A 4 x 2 mixed ANOVA revealed a significant interaction between Group and Study Duration (F(3,104) = 7.55, p < .001, η_p² = .18), indicating that the relative accuracy of groups varied as a function of study duration. For the 2 seconds condition, follow-up comparisons show that both SR-Weak and SR-Strict super-recogniser groups were more accurate than Examiners (SR-Weak vs. Examiners: t(48.3) = 3.91, p < .001, Cohen’s d = 1.02; SR-Strict vs. Examiners: t(32.6) = 4.71, p < .001, Cohen’s d = 1.33). There was a small numerical advantage for the SR-Strict (M = 0.91, SD = 0.04) over the SR-Weak group (M = 0.88, SD = 0.06; Mean difference = 0.02, Cohen’s d = 0.37).

For the 30 seconds condition, SR-Weak group (M = 0.94, SD = 0.04) and SR-Strict group (M = 0.95, SD = 0.03) were both statistically equivalent to Examiners (SR-Weak vs. Examiners: t(57.9) = 0.67, p = .506, Cohen’s d = 0.16; SR-Strict vs. Examiners: t(23.9) = 1.79, p = .086, Cohen’s d = 0.58). Again there was a small numerical advantage for the SR-Strict over SR-Weak group (Mean difference = 0.01, Cohen’s d = 0.37). Thus, super-recognisers were more accurate than examiners when viewing face images for very short durations (see also [23]).

Face inversion effect. Face inversion effects for each participant were calculated by subtracting AUC scores on inverted from upright face conditions, separately for 2 and 30 second. A 4x2 mixed factors ANOVA revealed a significant interaction between Group and Study duration (F(3,104) = 3.51, p = .018, η_p² = .09; see bottom panel of Fig 2). Follow-up comparisons for the 2 seconds condition showed SR-Weak (M = 0.18, SD = 0.08) and SR-Strict (M = 0.19, SD = 0.07) had larger inversion effects than Examiners, but were no different to Student controls (SR-Weak vs. Examiners: t(62.9) = 2.98, p = .004, Cohen’s d = 0.71; SR-Strict vs. Examiners: t(16.0) = 2.4, p = .028, Cohen’s d = 0.93; SR-Weak vs. Students: t(63.2) = 0.41, p = .684, Cohen’s d = 0.10; SR-Strict vs. Students: t(22.0) = 0.56, p = .578, Cohen’s d = 0.18).

For the 30 seconds condition, we observed a strikingly different pattern. Face Inversion Effects in SR-Weak (M = 0.11, SD = 0.06) and SR-Strict (M = 0.09, SD = 0.06) were no different to Examiners, and were significantly smaller than Student controls (SR-Weak vs. Examiners: t(62.4) = 0.09, p = .930, Cohen’s d = 0.02; SR-Strict vs. Examiners: t(16.1) = 1.07, p = .298, Cohen’s d = 0.41; SR-Weak vs. Controls: t(63.4) = 2.39, p = .020, Cohen’s d = 0.58; SR-Strict vs. Controls: t(21.0) = 2.84, p = .010, Cohen’s d = 0.91). Together, this pattern of results suggests that super-recognisers are as affected by inversion as control participants with shorter study durations but behave more like examiners at longer durations. This may indicate that super-recognisers are able to adapt their perceptual processing strategies depending on the demands of the task more readily than both examiners and controls.

2.2. Establishing the boundaries of super-recognisers’ superior abilities

Having established that our super-recogniser groups were comparable to existing face identification experts in perceptual face matching, we asked whether they were also superior in a broader range of tasks they might expected to perform in police work. The range of operational person identification tasks that police officers perform extends beyond the lab-based face identification tests that are typically used to measure ability [7], but the boundaries that define their superior perceptual and cognitive ability are yet to be fully established. For clarity, the following analyses compared only SR-Weak group–i.e. the full sample of SRs–to student controls, but similar patterns were observed when we limited the anlysis to only those SRs meeting the strict selection criteria (see S1 Appendix).

2.2.1 Matching person identity when the face is not visible

When matching images from CCTV, face information is often unavailable due to occlusion, head angle or poor image quality. Can super-recognisers also achieve superior accuracy when matching identifying information from the body? Previous work has shown an association between individual differences in face and body identification tasks [36], perhaps pointing to recruitment of a broader person identification system that recruits diverse sources of identity information [37]. We presented participants with two images of people taken at different times in different environmental settings and asked them to decide if they showed the same person or different people using a Likert scale. Images either showed only the internal features of the face (face only) or only information outside of the internal features (body only; see Fig 3 and methods for more details).

Visual inspection of Fig 3 shows that super-recognisers (SR-Weak) outperformed Student Controls in both Face Only and Body Only conditions, but this advantage was largest for Face Only images. A mixed-factor ANOVA confirmed this interaction was significant (F(1,82) = 12.99, p = .001, η_p² = .14). Simple main effects showed larger differences in accuracy for Face Only trials compared to Body Only trials (Face Only: Mean difference = 23%, t(81.4) = 7.32, p < .001, Cohen’s d = 1.56; Body Only: Mean difference = 13%, t(80.8) = 6.41, p < .001, Cohen’s d = 1.36). Full details of the ANOVA analysis are provided in S1 Appendix.

2.2.2 Memory for faces and the context in which they are encountered

All of the lab-based face identification tasks presented so far involved perceptual matching, not memory. However, in many surveillance and security scenarios, it is important to retain faces in memory. For example, police super-recognisers are often asked to commit large numbers of facial photos of suspects to memory prior to large public events, aiming to recognize them in the crowds [38].

For more spontaneous recognitions, for example when recognising a suspect in CCTV from a previous crime, it is also important for super-recognisers to remember where they know the face from. A police officer might for example recognise a person in a CCTV recording, but not realise the person works at their local coffee shop. So, we also tested their ability to link memory of faces to scenes in which they were encountered. Decades of memory research have established a distinction between recognition-based and recollection-based memory (e.g. [39]). Recollection is an intentional and more difficult form of memory processing compared with the automatic recognition memory. This distinction is often referred to as the ‘butcher on the bus’ phenomenon, because it captures the everyday experience of recognising someone that you know but being unable to recall where you know them from (see [40,41]). Whether super-recognisers are also better able to link face memories to context is therefore of practical importance and can help us understand the person-related memory representations associated with high accuracy in face recognition.

Face memory from police ‘photoboards’. To test face memory ability, we created an experimental analogue of ‘photoboards’ used to familiarise super-recognisers with people they would later be expected to recognise in the field. An example of the photoboard test is shown in Fig 4. In the study phase, participants were shown faces pictured in photoboards containing either 8 images from social media of a single face, or a single image of the face. In the subsequent test phase, they were shown videos of these individuals intermixed with foils that were not shown in the study phase.

Fig 4 — This test measures participants’ ability to learn faces from either one or multiple photographs. Example stimuli from the study phase (A) and test phase (B). Super-recognisers (SR-Weak) were more accurate than Student Controls when recognising faces learnt from one image or eight images but did not show any additional benefit from having access to more images.

Given that super-recognisers sample more information from faces [42] and make identity decisions relatively quickly [23], we predicted that their advantage over controls would be greatest for photoboards containing multiple images of the same face, which would enable them to construct more detailed identity representations in memory. Results did not support this prediction, with super-recognisers (SR-Weak) showing the same advantage over Controls in 1 images and 8 image conditions (main effect of photoboard size: F(1, 59) = 11.04, p = .002, η_p² = .16; non-sig interaction: F(1, 59) < 1, p = .933, η_p² = .00; see S1 Appendix for further details).

‘Butcher on the bus’: Face-in-place recognition memory test. Having verified superior face memory in super-recognisers, we then asked whether they also had an advantage when linking memories of faces to the visual scenes in which they were encountered. In this test, participants were asked to remember a series of unfamiliar faces superimposed on scenes. Later, they were shown these faces again mixed with unseen people, both of which were again superimposed on scenes. The studied faces were either shown on the same scenes as during study or on different scenes. They were asked to decide whether the face had been shown in the learning phase (i.e. face recognition accuracy). If they responded that they had seen the face during learning they were also asked whether it had appeared on the same or different scene to the one currently displayed (Face-in-place recognition; see Fig 5).

Results of the face-in-place recognition memory test are shown in Fig 5. For face recognition accuracy, there was a significant main effect of Group, with a higher hit rate in Super-recognisers (SR-Weak: M = 64.3; SD = 16.5) compared to Students (M = 49.6; SD = 14.7; F(1,56) = 14.05, p < .001, η_p² = .20), and a higher correct rejection rate (t(35.7) = 2.28, p = .028, Cohen’s d = 0.66; see S1 Appendix for full ANOVA analysis). Crucially, for accuracy on the subsequent ‘did you learn this face in this scene?’ decision, the difference between super-recognisers (M = 63.7; SD = 14.0) and Students (M = 62.9; SD = 16.4) was not significant (t(36.6) = 0.19, p = .854, Cohen’s d = 0.05). These results show that while super-recognisers were better at recognising faces seen in the learning phase regardless of whether they were seen in the same or different scene as in the learning phase, they were no better than controls at linking these faces to the correct visual scene.

2.2.3 UNSW House Test: Super-recognisers’ memory and matching ability for non-face images

In a final test, we examined whether super-recognisers were also better than controls in memory and matching tests involving images that did not contain people. To do this we tested them on the UNSW House Test, a version of the UNSW Face Test [33] that used images of houses instead of faces. Prior work has found that individual differences in face identity processing are somewhat related to abilities in processing other object types (e.g. [4,17,18,43]), suggesting that individual differences reflect graded expertise in visual processing rather than a sharp dissociation between more general visual processing and memory.

The UNSW House Test shares the same design as the UNSW Face Test and images of the same house are subject to the same image-level variability in camera angle, camera-to-subject-distance, lighting, etc. (see Fig 6 and Methods for further details on the test). Results of the UNSW House Test are shown in Fig 6. Super-recognisers (SR-Weak: M = 80.9%, SD = 15.2) were significantly more accurate than Students on this test (Students: M = 68.4%, SD = 13.6; t(57.6) = 3.44, p = .001, Cohen’s d = 0.86), indicating they also have an advantage on non-face identification tasks, supporting the proposal that a domain-general visual matching ability may partly explain their superior face identification abilities.

Fig 6 — The UNSW House Test examines whether super-recognisers’ superior face matching and memory ability generalises to non-face objects like houses. Super-recognisers (SR-Weak) were more accurate on this test than Student Controls, suggesting that some of their superiority with faces generalises to the recognition of non-face objects.

2.3. Summary performance profiles show benefits of stricter criteria when selecting groups of super-recognisers

Fig 7 shows aggregated test results for super-recognisers on the face identification tests reported in the previous sections. Performance is expressed as mean z-scores for each super-recogniser relative to control means and calculated separately for tests that required participants to memorise faces (2 tests, x-axis) and tests that required perceptual matching of face identity (5 tests, y-axis). For the purpose of this analysis, we omitted tests reported in this paper that: (i) were used as screening tests; (ii) were identification tests of non-face objects (UNSW House Test, Body only trials in Face & Body test); or (iii) were inverted face matching tests (2 and 30s Inverted EFCT).

Next, we compared the effect of using a strict versus weak criteria to select super-recognisers. Across the follow-up lab-based tests, the strict criteria group had an average performance advantage over controls of z = 1.68 for memory and z = 2.07 for matching while the weak criteria group had an average performance advantage of z = 1.23 for memory and z = 1.68 for matching. In terms of accuracy, the strict group was 17% better than norms on memory tests and 28% better than norms on matching tests. This benefit was lower for the weak criteria group, who scored 12% better than norms for memory and 22% better for matching. Comparing performance between the strict and weak criteria groups, strict criteria super-recognisers were consistently more accurate than the weak criteria super-recognisers, scoring between 1–8% better on average for individual tests. The consistency of strict criteria group’s outperformance of weak criteria was statistically reliable by a Wilcoxon sign test (strict criteria > weak criteria, T₇ = 2.37, p = .018).

Memory and matching performance were significantly positively correlated, Spearman’s rho (37) = .415, p = .01. However, it is also clear from visual inspection of Fig 7 that individuals appear in all four quadrants of this plot: either passing the weak criteria (>1.7 SD) for follow-up tests on both memory and matching (n = 10, 26%), matching only (n = 14, 37%) or less commonly on memory only (n = 2, 5%). Further, there was a substantial number of participants that passed initial criteria based on online screening, but did not pass criteria on either memory or matching tasks after subsequent lab-based testing (n = 12, 32%).

Numerically, aggregate scores across all follow-up tests show superior performance for all but two of the super-recognisers. All of the super-recognisers selected on the basis of strict criteria showed substantial superiority over controls, and also higher accuracy relative to super-recognisers selected with weaker criteria. This verifies that the ability of super-recognisers persists beyond initial screening test performance. In addition, the clear variability in Fig 7 test scores points to the importance of using follow-up tests to verify individuals’ ability (see also [31]) and applying stricter selection criteria using multiple tests to protect against selecting participants with average abilities.

3 Discussion

We tested face identification ability of over 1600 Australian police officers. From these, we selected a group of 38 for further lab-based testing that had achieved an average z-score of greater than +1.7 relative to normative accuracy on 3 standard tests. Prior studies have tested groups of super-recognisers that have been selected for roles in police forces based on face identity processing tests [20,21]. But this is the first full report, documented in a scientific journal, of a process used to select super-recognisers for a professional organisation, and verify their ability on a range of tests relating to operational practise.

Our results add to the cumulative evidence that super-recognisers can help improve face identification accuracy in applied settings [19,20,21,26,27]. As a group, super-recognisers consistently outperformed typical viewers across a range of lab-based face matching and memory tasks. On average they outperformed control participants by between 20% (weak criteria) and 25% (strict criteria) percentage points, which spans the same range as previous tests. This included high accuracy on a series of face matching tasks that were designed to reflect challenging decisions made in forensic settings, and this accuracy was equivalent to benchmark performance of forensic facial comparison expert groups.

Notably however, on these tests–which did not involve memory, and included tests with unlimited time to compare images–no super-recognisers, nor forensic examiners, achieved perfect accuracy. Given the highly selective criteria and large pool of staff we chose super-recognisers from, this result suggests that the effective upper-limit of human’s accuracy on challenging tasks–even those that do not require committing faces to memory–is substantially below 100%. Naturaly, memory demands led to lower accuracy, which should reject the claim that super-recognisers ‘never forget a face’.

Individually, all but two super-recognisers in the weak criteria condition achieved accuracy that was numerically greater than controls on average. However, consistent with previous work, we observed substantial variation in the accuracy of individual super-recognisers in both memory and matching tests (e.g., [20,31]; see Fig 7) despite using multiple screening tests with diverse task demands, spanning both memory and matching ability. This underlines the difficulty of using current tests to identify individual super-recognisers that will, substantially and consistently, outperform average performers in tasks reflective of everyday police work. The potential of this approach is constrained by the reliability of tests [19,44] and the extent to which different types of face recognition ability tests converge on a unitary cognitive trait (i.e., ‘convergent validity’, see [8,18]). Since we conducted this work, tests reporting improved reliability have been published (see [18], Table 1), and researchers have begun working towards a standard framework for selecting super-recognisers using test batteries with more diverse task demands and measures [25,45]. It will be important in future work to verify whether these improvements can lead to quantitative gains in the performance of super-recognisers relative to the cohort we selected here.

For now, we have shown that a simple way to improve the effectiveness of selection is to apply stricter selection criteria. For our strict criteria group, where participants had achieved z-scores exceeding 1.7 standard deviations above normative accuracy on each of the 3 screening tests, we found that all super-recognisers consistently exceeded average performance on subsequent lab-based tests. Being highly selective can therefore mitigate against problems caused by limits on test reliability and validity.

Top performers on our screening tests showed high levels of accuracy on a battery of lab-based tests designed to capture some face identification tasks that might be performed in police work. Our battery of tests was not exhaustive, and did not include many important tasks performed in police work, for example monitoring video [45,46], live photo-ID checks [47] and longer-term recognition memory [29]. Ongoing evaluation of the effectiveness of the selection process we have documented here should therefore include testing of super-recognisers across more of the tasks they perform in their daily work.

Although ongoing testing beyond a single follow-up session was not included as part of this project, this type of evaluation will be important in future. This would provide further evidence of the effectiveness of the selection processes outlined here in improving face identification accuracy in police operations. It is also important to examine how SRs’ performance changes over time with practice and training. In other forensic disciplines, fingerprint matching experts improve over time with training and experience [48] and interestingly, individual differences in ability prior to any training predicted their later accuracy. No such study has been conducted for forensic roles involving face processing, but we found comparable accuracy between trained facial examiners and super-recognisers, both here and in recent studies (e.g., [3,23]).

This suggests that super-recognisers could be selected for forensic examiner roles, but it remains unclear whether they would be amenable to forensic training (see 35, 50 for reviews). In separate work, we have also found evidence that SRs tend to make more high confidence errors relative to facial examiners [23,30]. This points to another important reason to test SR performance in detail after receiving training–to examine whether this overconfidence is attenuated by training and experience. High confidence errors can lead to serious negative outcomes in criminal justice settings, for example wrongful convictions, and so this is an important question to address in future work.

Another aim of our study was to begin defining boundary conditions of SR’s abilities. Previous work has shown that SRs tend to perform above average on visual processing and memory tasks that do not involve faces [20,23]. This is consistent with patterns of correlation between face and object processing in the broader ability spectrum [17,43,49]. Here we also found that SR groups outperformed controls on perceptual matching and memory tasks not involving faces, although individual SR performance was somewhat variable (c.f. [36]). The pattern of group difference appeared graded with respect to the type of object being processed, with face processing tasks showing the largest group difference, followed by bodies (Cohen’s d = 1.36) and buildings (Cohen’s d = 0.86). Similar graded patterns were found in a recent study where SRs showed advantage over controls in matching chimpanzee faces, which was reduced relative to their human face processing advantage, but greater than for fingerprint matching [23]. Together, this suggests that SRs possess a general aptitude for performing visual tasks, with their ability tuned towards face identity but affording residual benefit in similar visual processing tasks. Given pre-existing perceptual ability predicts later accuracy in fingerprint matching trainees [48], it would be of both applied and theoretical interest to know whether super-recognisers’ skills predispose them to becoming experts in other object matching domains.

Finally, despite SRs’ heightened ability to recognise faces, they had surprising difficulty in verifying the visual context that recognised faces had been presented in. Both super-recognisers and controls made approximately 40% errors in matching faces to the scenes they were presented in, demonstrating a clear boundary to SRs’ expertise. Conversely, a previous study of memory ‘athletes’ with exceptional abilities to form face-name associations, found that they were no better at recognising faces [50]. As well as apparent practical implications, this may also provide insight into the nature of SR representations of person identity more broadly. It implies that highly sensitive perceptual representations of faces do not produce stronger connections with associated information. This dissociation is perhaps surprising given recent neuroscientific evidence that faces evoke more widely distributed brain activation in people with higher ability in face identification [51,52]. Understanding how super-recognisers link face memories to broader memory networks, including related person identity information, is therefore an important direction for future work.

In summary, we have shown that face identity processing abilities of super-recognisers selected from a large police force transfer well to a variety of lab-based tasks that reflect police work. Setting strict criteria can protect against selecting individuals that do not continue to show high levels of accuracy in subsequent testing. Some important qualifications remain. We have established boundary conditions that constrain the application of super-recognisers as a general solution for person identification tasks. Additionally, we have not evaluated performance of super-recognisers ‘on the job’, nor over a prolonged period. Because our lab-based tests did not capture the full diversity of operational tasks super-recognisers might be expected to perform, this type of work will be necessary in future to validate super-recogniser deployment in more complex real-world tasks.

4 Materials and methods

4.1 Ethics statement

This research was approved by the Deputy Vice Chancellor (Research) of the University of New South Wales on the advice of the Human Research Ethics Advisory Panel. All participants gave their informed consent either digitally or in writing. The faces of the individuals depicted in the figures have given written informed consent (as outlined in PLOS consent form) to publish and use their faces for these purposes.

4.2 Participants

Approximately 17,000 staff NSW Police Force staff were invited to complete three screening tests that assessed their face identification ability (see below for details). At the end of phase 1, 2537 staff completed at least 1 screening test, and 1670 completed all 3. Top performing staff who completed all 3 screening tests were then invited to participate in additional lab-based testing in-person at the UNSW Sydney campus.

Average scores of police officers on the screening tests are shown along with normative data in Table 1. Police officers were equivalent to published normative scores for both the CFMT+ (t(2042) = 0.22, p = .822, Cohen’s d = 0.05) and the UNSW Face Test (t(2030) = 1.55, p = .122, Cohen’s d = 0.12) but were 8.2% more accurate on average than published normative scores for the GFMT (t(2657) = 13.24, p < .001, Cohen’s d = 0.85). This may reflect the fact that GFMT is a self-paced test and so could be more susceptible to differences in participant motivation (but see [47]), but there also appears to be a general tendency for participant groups to score above normative in recent tests (see [16,53]).

To determine the effect of different super-recogniser selection thresholds, we recruited participants who satisfied either a strict (SR-Strict) or weak (SR-Weak) criteria. Individuals meeting SR-Strict criteria scored a minimum Z score of 1.7 SD on each screening test. Fifteen of 1651 participants (0.9%) satisfied these criteria and 11 participated in the lab tests (M_age = 39.2; 5 female, 6 male). Individuals meeting SR-weak criteria scored a minimum average Z score of 1.7 SD across the 3 screening tests. An additional 39 (2.4%) of the 1651 participants met SR-weak (not including those in the SR-Strict group) and 27 of those participated in the lab tests (Weak-only: M_age = 37.3; 10 female, 17 male). The ratio of female to male super-recognisers was proportionate to the composition of the NSW Police Force that completed all 3 the screening tests (female = 39%, male = 60%, other or prefer not to say 1%). This suggests that the selection process resulted in a gender balance that reflected the initial test cohort (for female:male ratio of faces used in our tests see S1 Appendix).

4.3 Screening tests

All staff members of the NSW Police Force were invited to participate in the super-recogniser screening research program via email and a notice on their intranet home page. Clicking a link in the advertisement directed participants to a central website where they could provide informed consent to participate and access 3 standardised tests of face identification ability. Participants were instructed that they could complete the tests in any order and in separate sessions, but that after commencing a test they must complete it in a single session. Participants were not given any feedback or told their scores on each test until the end of the screening test period, which concluded after a month. The three screening tests were as follows.

Cambridge Face Memory Test (CFMT+)

The CFMT+ (7) is a standardised test of unfamiliar face memory ability. Participants’ learn and recognize unfamiliar faces in a three-alternative-forced-choice test. The test contains four stages that successively increase the difficulty of the task by introducing either untrained views and lighting conditions of the images or by adding visual noise to the images. Accuracy is computed from the percentage of trials with correct identifications out of 102 and normative data for this test was sourced from (7).

Glasgow Face Matching Test (GFMT)

In the short version of the GFMT [4], participants match 40 face pairs (20 match, 20 nonmatch) by deciding whether they show the same person or two different people. Images of the same person were captured on the same day, just minutes apart, but with different cameras. Accuracy is computed from the percentage of trials with correct decisions out of 40. Normative data for this test was sourced from (4).

UNSW Face Test

The UNSW Face Test [33] is a screening test calibrated to be very difficult and so is suited to screening for identify super-recognisers. It consists of two tasks completed in a fixed order: a recognition memory task and a match-to-sample sorting task. For further details of this test see S1 Appendix. Accuracy is computed from the percentage of correct recognition and sort decisions out of 120. Normative data for this test was sourced from [33].

4.4 Comparing police super-recognisers to facial examiners on challenging face identification tests in the lab verifies their superior ability

Further details of the tests described below, performance measures and extended reporting of analysis are available in S1 Appendix. All follow up comparisons were conducted using Welch’s t-test as it is generally best when there is a difference between the variance of two populations and also when their sample sizes are unequal.

4.4.1 Facial recognition candidate list test

The facial recognition candidate list test models the daily work of passport issuance officers using facial recognition technology to screen for passport fraud and has been used in prior work to test the accuracy of facial examiners ([22]: Experiment 2). The test requires participants to identify whether a target face appears in a gallery of eight faces and to select the correct target when present. Target faces appear in the candidate list on 50% of trials in a random position.

4.4.2 Selfie-to-passport test

The Selfie-to-passport test was designed to measure accuracy on images that were the most difficult for facial recognition algorithms to classify. This test contained a total of 53 trials– 19 match and 34 nonmatch trials consisting of various ethnicities representative of the Australian population. On each trial, participants were presented with two face images simultaneously–one ‘selfie’ image and one passport image.

4.4.3 Expertise in facial comparison test (EFCT)

The EFCT is a pairwise unfamiliar face matching test that has previously been used to test an international group of forensic facial examiners (see [32]). The EFCT involves completing the same two subtests twice: first with images shown on screen for 2-seconds each, and later with images shown on screen for 30-seconds each. All subtests contain 84 face pairs (half same-identity, half different identity) and one is completed with faces shown upright, and the other with faces inverted.

4.5 Establishing the boundaries of super-recognisers’ superior abilities

Further details of the tests described below, performance measures and extended reporting of analysis are available in S1 Appendix.

4.5.1 Face and body matching test

The Face and body matching test examined participants’ ability to identify people based on only their face or only their body. This test consisted of 40 image pairs, 20 that were of the same person (match) and 20 of two different people (nonmatch). These images were sourced from the Person Identification Challenge Test ss32] but were cropped here to show only the face or only the body (see Fig 3 for example), resulting in a total of 80 image pairs (40 face only, 40 body only).

Forty-six undergraduate students were recruited as control participants for this test in exchange for course credit. This sample consisted of 29 females and 17 males aged between 18 and 24 (mean age = 19.0, SD = 1.3).

4.5.2 Photoboard recognition memory test

We designed a bespoke test of face memory performance to model a scenario where police officers are asked to memorise faces studied on ‘photoboards’, and to later recognise them (see Fig 4 for example).

This test was separated into 2 blocks, with a study phase, filler task and test phase in each block. The study phase contained 16 identities for the participants to remember. Each identity was shown in 1 single image or 8 different images for a total of 10 seconds. Following the study phase, participants were asked to complete a filler word-search task for 5 minutes. Then in the test phase, participants saw 2-second videos of 16 studied and 16 unstudied faces that showed the person either slowly turning towards the camera (90–0°) or away from camera (0–90°). For each video, participants responded whether or not they had studied that person previously. Half the videos showed previously studied faces captured in a different location to the photos shown in the learning phase, and the other half showed novel identities. This process was repeated again with a new set of 16 faces, and another word-search filler task. The faces assigned to each condition were counterbalanced between participants.

Twenty-three undergraduate students were recruited as control participants for this test in exchange for course credit. This sample consisted of 14 females (13 males) aged between 18 and 25 (mean age = 19.1, SD = 1.5).

4.5.3 Face-in-place recognition memory test

The face-in-place recognition memory test was developed to examine the extent to which people remember or use the visual context in which they first encountered a face during a subsequent recognition test. In a study phase, participants were presented with a sequence of 20 face images placed in the center of a visual scene (see Fig 5 for an example).

This test was composed of a study phase, and a test phase. In each trial of the study phase, participants saw a face image placed in the center of a visual scene (see Fig 5 for an example). Participants were presented with a total of 20 images, each showing a different person in a different scene. Images were shown in a randomized order for 10 seconds each. The test phase consisted of 80 trials split into the following conditions: 20 of the studied faces were shown twice in the test phase but in different images, once on the same background as the study phase and once on a different scene to the study phase (total of 40 match trials, 20 on the same scene, 20 on a different scene). Participants were also shown 40 foil faces randomly intermixed with the studied faces, with the foil faces all shown on one of the scenes shown with studied faces (total of 40 nonmatch trials).

In each trial of the test phase, participants were shown a face in a scene, and asked to decide if they recognised the face from the study phase (face recognition accuracy). When participants said they recognised the face, they were then asked if they had seen that face on the same scene (face-in-place recognition).

Twenty-one undergraduate students were recruited as control participants for this test in exchange for course credit. This sample consisted of 13 females and 8 males aged between 18 and 21 (mean age = 19.0, SD = 1.0).

4.5.4 UNSW House Test

The UNSW House Test was developed to test participants’ matching accuracy for non-face objects using the same format as the UNSW Face Test. This test therefore allows us to measure the extent to which performance on the UNSW Face Test is related to face-specific processing, compared to non-face object processing. The UNSW House Test contains a Recognition Memory Task and a Match-to-Sample Sorting task, completed in the same order for each participant. The images of the same house are subject to the same image-level variability in camera angle, camera-to-subject-distance, lighting etc. Accuracy is computed from the percentage of correct recognition and sort decisions out of 120. Fig 6 shows illustrative examples of images used in this test and further details are available in S1 Appendix.

Twenty-six undergraduate students were recruited as control participants for this test in exchange for course credit. This sample consisted of 17 females and 9 males aged between 18 and 24 (mean age = 19.0, SD = 1.5).

Supporting information

S1 Appendix. Additional data and analysis.

(DOCX)

Click here for additional data file.^{(79.4KB, docx)}

Acknowledgments

Thank you to Daniel Guilbert, Gabrielle Killen, Bojana Popovic, Melissa Bebbington, Alicia Jung, William Hoang, and Victor Varela for assistance with data collection, and to Daniel Guilbert for assistance developing the UNSW House Test.

Data Availability

The data that support the findings of this study are openly available on the Open Science Framework (https://doi.org/10.17605/OSF.IO/BJDWK).

Funding Statement

This research was supported by Australian Research Council funding to White (DP190100957; FT200100353) and private funding from the New South Wales Police Force. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

1.Fussey P, Davies B, Innes M. ‘Assisted’ facial recognition and the reinvention of suspicion and discretion in digital policing. The British Journal of Criminology. 2021;61(2):325–44. [Google Scholar]
2.Kemp RI, Towell N, Pike G. When Seeing should not be Believing: Photographs, Credit Cards and Fraud. Applied Cognitive Psychology. 1997;11:211–22. [Google Scholar]
3.Phillips PJ, Yates AN, Hu Y, Hahn CA, Noyes E, Jackson K, et al. Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms. Proc Natl Acad Sci U S A. 2018;115(24):6171–6. doi: 10.1073/pnas.1721355115 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Burton AM, White D, McNeill A. The Glasgow Face Matching Test. Behav Res Methods. 2010;42(1):286–91. doi: 10.3758/BRM.42.1.286 [DOI] [PubMed] [Google Scholar]
5.White D, Towler A, Kemp RI. Understanding professional expertise in unfamiliar face matching. In: Bindemann M, editor. Forensic Face Matching: Research and Practice: Oxford Scholarship Online; 2021. [Google Scholar]
6.Duchaine BC, Nakayama K. Developmental prosopagnosia: a window to content-specific face processing. Curr Opin Neurobiol. 2006;16(2):166–73. doi: 10.1016/j.conb.2006.03.003 [DOI] [PubMed] [Google Scholar]
7.Russell R, Duchaine B, Nakayama K. Super-recognizers: people with extraordinary face recognition ability. Psychon Bull Rev. 2009;16(2):252–7. doi: 10.3758/PBR.16.2.252 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.White D, Burton AM. Individual differences and the multidimensional nature of face perception. Nature Reviews Psychology. 2022;1(5):287–300. [Google Scholar]
9.Bate S, Portch E, Mestry N. When two fields collide: Identifying "super-recognisers" for neuropsychological and forensic face recognition research. Q J Exp Psychol (Hove). 2021;74(12):2154–64. doi: 10.1177/17470218211027695 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Duchaine B, Nakayama K. The Cambridge Face Memory Test: results for neurologically intact individuals and an investigation of its validity using inverted face stimuli and prosopagnosic participants. Neuropsychologia. 2006;44(4):576–85. doi: 10.1016/j.neuropsychologia.2005.07.001 [DOI] [PubMed] [Google Scholar]
11.Wilmer JB, Germine L, Chabris CF, Chatterjee G, Williams M, Loken E, et al. Human face recognition ability is specific and highly heritable. Proc Natl Acad Sci U S A. 2010;107(11):5238–41. doi: 10.1073/pnas.0913053107 [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Zhu Q, Song Y, Hu S, Li X, Tian M, Zhen Z, et al. Heritability of the specific cognitive ability of face perception. Curr Biol. 2010;20(2):137–42. doi: 10.1016/j.cub.2009.11.067 [DOI] [PubMed] [Google Scholar]
13.Shakeshaft NG, Plomin R. Genetic specificity of face recognition. Proc Natl Acad Sci U S A. 2015;112(41):12887–92. doi: 10.1073/pnas.1421881112 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Golarai G, Ghahremani DG, Whitfield-Gabrieli S, Reiss A, Eberhardt JL, Gabrieli JD, et al. Differential development of high-level visual cortex correlates with category-specific recognition memory. Nat Neurosci. 2007;10(4):512–22. doi: 10.1038/nn1865 [DOI] [PMC free article] [PubMed] [Google Scholar]
15.McGugin RW, Newton AT, Tamber-Rosenau B, Tomarken A, Gauthier I. Thickness of Deep Layers in the Fusiform Face Area Predicts Face Recognition. J Cogn Neurosci. 2020;32(7):1316–29. doi: 10.1162/jocn_a_01551 [DOI] [PubMed] [Google Scholar]
16.White D, Guilbert D, Varela VPL, Jenkins R, Burton AM. GFMT2: A psychometric measure of face matching ability. Behav Res Methods. 2022;54(1):252–60. doi: 10.3758/s13428-021-01638-x [DOI] [PubMed] [Google Scholar]
17.McCaffery JM, Robertson DJ, Young AW, Burton AM. Individual differences in face identity processing. Cogn Res Princ Implic. 2018;3:21. doi: 10.1186/s41235-018-0112-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Wilmer JB, Germine L, Chabris CF, Chatterjee G, Gerbasi M, Nakayama K. Capturing specific abilities as a window into human individuality: the example of face recognition. Cogn Neuropsychol. 2012;29(5–6):360–92. doi: 10.1080/02643294.2012.753433 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Ramon M, Bobak AK, White D. Super-recognizers: From the lab to the world and back again. Br J Psychol. 2019;110(3):461–79. doi: 10.1111/bjop.12368 [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Davis JP, Lander K, Evans R, Jansari A. Investigating Predictors of Superior Face Recognition Ability in Police Super-recognisers. Applied Cognitive Psychology. 2016;30(6):827–40. [Google Scholar]
21.Robertson DJ, Noyes E, Dowsett AJ, Jenkins R, Burton AM. Face Recognition by Metropolitan Police Super-Recognisers. PLoS One. 2016;11(2):e0150036. doi: 10.1371/journal.pone.0150036 [DOI] [PMC free article] [PubMed] [Google Scholar]
22.White D, Dunn JD, Schmid AC, Kemp RI. Error Rates in Users of Automatic Face Recognition Software. PLoS One. 2015;10(10):e0139827. doi: 10.1371/journal.pone.0139827 [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Towler A, Dunn JD, Martínez SC, Moreton R, Eklöf F, Ruifrok A, et al. Diverse routes to expertise in facial recognition. PsyArXiv. 2021. [Google Scholar]
24.Rjosk S. Super-Recognizers in (Berlin) law enforcement. Neuropsychologia. 2021;158:107806. doi: 10.1016/j.neuropsychologia.2021.107806 [DOI] [PubMed] [Google Scholar]
25.Nador JD, Vomland M, Thielgen MM, Ramon M. Face recognition in police officers: Who fits the bill? Forensic Science International: Reports. 2022;5. [Google Scholar]
26.Balsdon T, Summersby S, Kemp RI, White D. Improving face identification with specialist teams. Cogn Res Princ Implic. 2018;3:25. doi: 10.1186/s41235-018-0114-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Bobak AK, Dowsett AJ, Bate S. Solving the Border Control Problem: Evidence of Enhanced Face Matching in Individuals with Extraordinary Face Recognition Skills. PLoS One. 2016;11(2):e0148148. doi: 10.1371/journal.pone.0148148 [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Noyes E, Phillips PJ, O’Toole A. What is a super-recogniser? Face processing: Systems, disorders and cultural differences: Nova Science Publishers Inc; 2017. p. 173–201. [Google Scholar]
29.Davis JP, Bretfelean LD, Belanova E, Thompson T. Super-recognisers: Face recognition performance after variable delay intervals. Applied Cognitive Psychology. 2020;34(6):1350–68. [Google Scholar]
30.Hahn CA, Tang LL, Yates AN, Phillips PJ. Forensic facial examiners versus super‐recognizers: Evaluating behavior beyond accuracy. Applied Cognitive Psychology. 2022;36(6):1209–18. [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Ramon M. Super-Recognizers—a novel diagnostic framework, 70 cases, and guidelines for future work. Neuropsychologia. 2021;158:107809. doi: 10.1016/j.neuropsychologia.2021.107809 [DOI] [PubMed] [Google Scholar]
32.White D, Phillips PJ, Hahn CA, Hill M, O’Toole AJ. Perceptual expertise in forensic facial image comparison. Proc Biol Sci. 2015;282(1814). doi: 10.1098/rspb.2015.1292 [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Dunn JD, Summersby S, Towler A, Davis JP, White D. UNSW Face Test: A screening tool for super-recognizers. PLoS One. 2020;15(11):e0241747. doi: 10.1371/journal.pone.0241747 [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Towler A, White D, Kemp RI. Evaluating the feature comparison strategy for forensic face identification. J Exp Psychol Appl. 2017;23(1):47–58. doi: 10.1037/xap0000108 [DOI] [PubMed] [Google Scholar]
35.Towler A, Kemp RI, White D. Can Face Identification Ability Be Trained? Evidence for Two Routes to Expertise. Forensic face matching: Research and practice. 2021:89. [Google Scholar]
36.Noyes E, Hill MQ, O’Toole AJ. Face recognition ability does not predict person identification performance: using individual data in the interpretation of group results. Cogn Res Princ Implic. 2018;3:23. doi: 10.1186/s41235-018-0117-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Yovel G O ’Toole AJ. Recognizing People in Motion. Trends Cogn Sci. 2016;20(5):383–95. doi: 10.1016/j.tics.2016.02.005 [DOI] [PubMed] [Google Scholar]
38.Davis J, Robertson D. Capitalizing on the super-recognition advantage: a powerful, but underutilized, tool for policing and national security agencies. Journal of the Homeland Defense & Security Information Analysis Centre. 2020;7(1):20–5. [Google Scholar]
39.Jacoby LL. A process dissociation framework: Separating automatic from intentional uses of memory. Journal of Memory and Language. 1991;30(5):513–41. [Google Scholar]
40.Mandler G. Recognizing—the Judgment of Previous Occurrence. Psychological Review. 1980;87(3):252–71. [Google Scholar]
41.Gruppuso V, Lindsay DS, Masson ME. I’d know that face anywhere! Psychon Bull Rev. 2007;14(6):1085–9. doi: 10.3758/bf03193095 [DOI] [PubMed] [Google Scholar]
42.Dunn JD, Varela VPL, Nicholls VI, Papinutto M, White D, Miellet S. Face-Information Sampling in Super-Recognizers. Psychol Sci. 2022;33(9):1615–30. doi: 10.1177/09567976221096320 [DOI] [PubMed] [Google Scholar]
43.Richler JJ, Tomarken AJ, Sunday MA, Vickery TJ, Ryan KF, Floyd RJ, et al. Individual differences in object recognition. Psychol Rev. 2019;126(2):226–51. doi: 10.1037/rev0000129 [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Young AW, Noyes E. We need to talk about super-recognizers Invited commentary on: Ramon M., Bobak A. K., & White D. Super-recognizers: From the lab to the world and back again. British Journal of Psychology. Br J Psychol. 2019;110(3):492–4. [DOI] [PubMed] [Google Scholar]
45.Thielgen MM, Schade S, Bose C. Face processing in police service: the relationship between laboratory-based assessment of face processing abilities and performance in a real-world identity matching task. Cogn Res Princ Implic. 2021;6(1):54. doi: 10.1186/s41235-021-00317-x [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Davis JP, Forrest C, Treml F, Jansari A. Identification from CCTV: Assessing police super-recogniser ability to spot faces in a crowd and susceptibility to change blindness. Applied Cognitive Psychology. 2018;32(3):337–53. [Google Scholar]
47.White D, Kemp RI, Jenkins R, Matheson M, Burton AM. Passport officers’ errors in face matching. PLoS One. 2014;9(8):e103510. doi: 10.1371/journal.pone.0103510 [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Searston RA, Tangen JM. The Emergence of Perceptual Expertise with Fingerprints Over Time. Journal of Applied Research in Memory and Cognition. 2017;6(4):442–51. [Google Scholar]
49.Growns B, Dunn JD, Mattijssen E, Quigley-McBride A, Towler A. Match me if you can: Evidence for a domain-general visual comparison ability. Psychon Bull Rev. 2022;29(3):866–81. doi: 10.3758/s13423-021-02044-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
50.Ramon M, Miellet S, Dzieciol AM, Konrad BN, Dresler M, Caldara R. Super-Memorizers Are Not Super-Recognizers. PLoS One. 2016;11(3):e0150972. doi: 10.1371/journal.pone.0150972 [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Elbich DB, Scherf S. Beyond the FFA: Brain-behavior correspondences in face recognition abilities. Neuroimage. 2017;147:409–22. doi: 10.1016/j.neuroimage.2016.12.042 [DOI] [PubMed] [Google Scholar]
52.Ambrus GG, Eick CM, Kaiser D, Kovacs G. Getting to Know You: Emerging Neural Representations during Face Familiarization. J Neurosci. 2021;41(26):5687–98. doi: 10.1523/JNEUROSCI.2466-20.2021 [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Towler A, Kemp RI, Burton AM, Dunn JD, Wayne T, Moreton R, et al. Do professional facial image comparison training courses work? PLoS One. 2019;14(2):e0211037. doi: 10.1371/journal.pone.0211037 [DOI] [PMC free article] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0283682.r001

Decision Letter 0

Rocco Palumbo

26 Dec 2022

PONE-D-22-32244Selecting police super-recognisersPLOS ONE

Dear Dr. White,

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Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

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Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This is a very interesting paper describing selection of super-recognisers from a large organisation. While most of the work in this area tends to be descriptive or small-group lab-based, the authors have offered a major innovation here. This has the potential to become a very influential paper, and I recommend publication. Its main contributions are (i) description of organisational-level SR-selection; and (ii) the very interesting finding that SR performance does not generalise well to other tasks that seem related.

I have only two main comments to make.

First, I am a little confused about the direct statistical comparison of SR-Strict and SR-Weak groups (for example line 295 et passim). Am I right to think that the Strict group is a subset of the Weak group? It is hard to work this out, because of the non-integer df values (presumably due to a statistical correction?). If one of the SR groups is a subset of the other, then it is not straightforward to see how these can be compared with a simple t-test. Of course, it is easy to see how each SR group can be compared with other groups (e.g. line 291 et passim), and the size of any difference can be discussed. Those are straightforward between-subjects comparisons, but the comparison of overlapping groups is not like this. Perhaps I am mistaken here, and the authors can clarify the procedure.

Second, I was not convinced that the summary beginning on line 212 was very helpful. The problem is that the reader is presented with aggregate data before knowing what the constituent tests were. This would be fine if it were straightforward, but even the aggregation comes with qualifications (i.e. about excluded tests – line 220), and these qualifications do not really make sense until one has read the detailed material that follows. In fact, I also found presentation of Fig 1 in this section to be a bit unhelpful here. It means that the very first visual presentation of data encourages the reader to consider an association between memory and matching that is not at all central to the purpose of the paper. For me, figure 2 would be a much better place to start. So, I would suggest that the authors consider putting the material in the section starting line 212 towards the end of their results section, not at the beginning. Note that this will not introduce narrative tension, as the results have already been summarised in an earlier section (line 160), which is good. I emphasise that this is just a suggestion for readability – not a condition for acceptance.

Finally, I noticed a typo on line 168: “consistently higher” missing verb

Reviewer #2: Review of the manuscript entitled “Selecting police super-recognisers” by White, Dunn, Towler and Kemp

This is a very interesting study in which the Authors exploited a dozen of validated paradigms to investigate facial perception and memory in a group of Australian police “super-recognisers” (SRs). The sample was divided into two subgroups: weak and strict SRs and their performance was compared to normative data or, when these data were not available, with students’ performance. The study is well-conceived and the manuscript well-written. The set of paradigms explored an interesting set of different skills in this specific population, and I am happy to endorse the publication of this work, after suggesting some minor hints which could help in further improve the clarity of the study:

1. The final sample is not so wide to allow the authors to include Sex as a between-subjects factor, but some more details on the gender of the stimuli would be useful for each paradigm. Is there a possible gender difference (both sample and stimuli) in such a domain? This topic should be at least named in the manuscript.

2. In the Introduction, brain and physiology difference in face recognition and face manipulation are named – I suggest to insert a further citation on hemispheric asymmetries for face recognition and spatial manipulation: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4671171/

3. To facilitate the reading of the manuscript, the set of paradigm could be numbered (from 1 to 12, including selection criteria).

4. Page 12 starts with a significant 3-way ANOVA, but then two separate 2-way ANOVA are described. Since the 3-way interaction is significant, I strongly suggest reporting the overall interaction without splitting into two different analyses (which makes it impossible to see the real interaction among the three factors).

5. Line 344: is “Exposure duration” the same factor named “Study duration” some lines above? Please, check for consistency in variable names.

6. Line 353: the citation is not written according to the journal standard (numbers).

7. Page 14, line 413: the ANOVA interaction was significant: please, insert post-hoc comparisons before other (main effects) significant results.

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2023 May 17;18(5):e0283682. doi: 10.1371/journal.pone.0283682.r002

Author response to Decision Letter 0

19 Jan 2023

James D. Dunn, Alice Towler, Richard I. Kemp, and David White

Author response to editor and reviewer’s manuscript PONE-D-22-32244

Selecting police super-recognisers

Author reply is denoted by ** and bold text. All page and line references refer to the tracked changes version.

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**Manuscript formatting has been updated to adhere to style requirements.

For additional information about PLOS ONE ethical requirements for human subjects research, please refer to http://journals.plos.org/plosone/s/submission-guidelines#loc-human-subjects-research.

**Ethics statement has been added under the Method section.

**Caption for supporting information file have been added to the end of the manuscript before the results section.

4. We note that Figure 2 includes an image of a participant in the study.

If you are unable to obtain consent from the subject of the photograph, you will need to remove the figure and any other textual identifying information or case descriptions for this individual.

**We confirm that consent has been given from the individual’s pictures to use their image for publication. Note that we have switched the images in Fig 5 for images of a different person who has signed informed consent. The ethics statement now includes a statement about the faces in the figures: “The faces of the individuals depicted in the figures have given written informed consent (as outlined in PLOS consent form) to publish and use their faces for these purposes.”

**Reference list has been reviewed and is correct.

Comments to the Author

Reviewer #1

This is a very interesting paper describing selection of super-recognisers from a large organisation. While most of the work in this area tends to be descriptive or small-group lab-based, the authors have offered a major innovation here. This has the potential to become a very influential paper, and I recommend publication. Its main contributions are (i) description of organisational-level SR-selection; and (ii) the very interesting finding that SR performance does not generalise well to other tasks that seem related.

I have only two main comments to make.

** The reviewer is correct that statistical comparison of the strict V weak criteria SR groups is complicated by the fact that the SR-strict group is a subset of the SR-weak group. On the other hand, comparing the relative average accuracy of these groups is important practically, because it quantifies the accuracy that would be expected given two selection criteria. As a solution, we have removed direct t-test comparisons in favour of reporting the effect size of the differences in individual tests, and we refer the reader to the summary analysis at the end for a formal analysis (which we have now moved to the end following R1’s next comment).

Regarding the non-integer df values, we used a Welch t-test for all the planned comparisons (this is now noted on pg 26 ln 715). Welch's t-test also known as unequal variances t-test is generally applied when there is a difference between the variance of two populations and also when their sample sizes are unequal. We now note this near line 933.

** On reflection we agree with the reviewer and thank them for the suggestion, the summary analysis is now reported at the end of the Methods and Results section.

Finally, I noticed a typo on line 168: “consistently higher” missing verb

**This error has been corrected.

Reviewer #2

** All tests had roughly equal numbers of male and female faces (varying between 40:60 – 60:40 in ratio) except for the CFMT+ which only contains male faces. Below is a table showing the female:male ratio in each test which shows a consistently even ratio across tests.

Test % of female faces in test

GFMT 40:60

UNSW Face Test 50:50

Facial recognition candidate list test 50:50

Selfie-to-passport test 60:40

EFCT Upright 54:46

EFCT Inverted 61:39

Face and body matching test 40:60

Photoboard recognition memory test 50:50

Face-in-place recognition memory test 50:50

Gender differences in face identity processing individual difference tests tend to be very small (e.g. S1 Appendix in Dunn et al., 2020), and not consistently observed across studies either in the presence of a difference or the direction.

To test if there was any evidence that our selection process was biased towards selecting one gender over another, we compared the female:male ratio for the selected super-recognisers (39:61) to the female:male ratio of the NSW Police Force officers that completed all 3 the screening tests (40:60). This suggests that the selection process resulted in a gender balance that reflected the initial test cohort.

** The reference to physiology here was relating to associations between physiology and face identity processing ability. While we do agree that relationship between hemispheric asymmetries and face identity processing ability would be an interesting research topic, as the study cited here does not appear to directly test this association, we have opted not to cite in our introduction.

3. To facilitate the reading of the manuscript, the set of paradigm could be numbered (from 1 to 12, including selection criteria).

**We considered this as it may have made cross-referencing easier, but after making the necessary edits we felt that it was not helpful and perhaps distracting. So we have retained the headings from the previous version of the ms.

** The reviewer is correct that our ‘follow-up’ analysis did not address the nature of the 3-way interaction. However, we believe that the original presentation of the results somewhat mischaracterised our approach to analysis. In fact, we had intended a priori to analyse the effects of Study Duration and the Inversion Effect separately and this was not in fact a follow-up of the 3-way ANOVA.

We have revised this results section (from line 378) to make this clearer. We now report the full 3-factor ANOVA model in the Supplementary Materials with a Supplementary Figure to aid interpretation of the full ANOVA model. We believe the results of the 3-way ANOVA are consistent with the analysis reported in the main paper but that computing face inversion effects for individual participants aids interpretation of the pattern of results.

5. Line 344: is “Exposure duration” the same factor named “Study duration” some lines above? Please, check for consistency in variable names.

**All instances of ‘Exposure duration’ have been corrected to Study duration.

6. Line 353: the citation is not written according to the journal standard (numbers).

**This reference has been corrected.

7. Page 14, line 413: the ANOVA interaction was significant: please, insert post-hoc comparisons before other (main effects) significant results.

**The tests that are reported in this section were already reporting simple main effects. Main effects are reported in supplementary materials as these were qualified by the sig interaction. We have added a reference to the supplementary materials here as it was missing.

Attachment

Submitted filename: SuperRecognisersNSWPF_ResponseToReviewers.pdf

Click here for additional data file.^{(160.2KB, pdf)}

PLoS One. doi: 10.1371/journal.pone.0283682.r003

Decision Letter 1

Rocco Palumbo

20 Feb 2023

PONE-D-22-32244R1Selecting police super-recognisersPLOS ONE

Dear Dr. White,

Please submit your revised manuscript by Apr 06 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Rocco Palumbo

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: (No Response)

Reviewer #2: Yes

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6. Review Comments to the Author

Reviewer #1: (No Response)

Reviewer #2: Review 2 of the manuscript entitled “Selecting police super-recognisers” by White, Dunn, Towler and Kemp

Some of my previous comments have been not considered by the Authors, I would like to suggest a further effort in considering them. Specifically, from my previous points:

Previous point 1. Sex difference should be at least named in the manuscript:

- A personal reply has been added in the letter, but Point 1 is anyway omitted in the manuscript. I suggest inserting this point also in the discussion, also by inserting the explanations provided in the letter.

Previous point 2. A further citation on hemispheric asymmetries for face recognition and spatial manipulation: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4671171/

- I understand that hemispheric asymmetries are not crucial for the present study, but I believe the suggested citation can be interesting for a potential reader of this manuscript. Obviously, the Authors are free to decide whether this reference is of interest – my personal opinion is favourable to this option.

Previous point 3. To facilitate the reading of the manuscript, the set of paradigms could be numbered:

- I still believe that the numbering would make it much easier to follow the manuscript.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

**********

PLoS One. 2023 May 17;18(5):e0283682. doi: 10.1371/journal.pone.0283682.r004

Author response to Decision Letter 1

6 Mar 2023

Author reply is denoted by **. All page and line references refer to the tracked changes version.

Reviewer 1

Reviewer 1 did not have additional comments on this revision.

Reviewer 2

Some of my previous comments have been not considered by the Authors, I would like to suggest a further effort in considering them. Specifically, from my previous points:

Previous point 1. Sex difference should be at least named in the manuscript:

**We have now added an additional sentence describing these sex differences in the manuscript (Participant’s line 679) and included female:male ratios of faces in the tests to Supplementary Materials (Supplementary Table 2).

Previous point 2. A further citation on hemispheric asymmetries for face recognition and spatial manipulation: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4671171/

**We continue to believe that this paper is not sufficiently relevant to our study to warrant inclusion in the reference list because it is not about individual differences in face recognition ability.

Previous point 3. To facilitate the reading of the manuscript, the set of paradigms could be numbered:

- I still believe that the numbering would make it much easier to follow the manuscript.

**We have now numbered the subsections of Method & Results and refer to these in the final paragraph of the introduction.

Attachment

Submitted filename: SuperRecognisers_NSWPF_responseToReviewersR2.docx

Click here for additional data file.^{(15.6KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0283682.r005

Decision Letter 2

Rocco Palumbo

14 Mar 2023

Selecting police super-recognisers

PONE-D-22-32244R2

Dear Dr. White,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Rocco Palumbo

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0283682.r006

Acceptance letter

Rocco Palumbo

23 Mar 2023

PONE-D-22-32244R2

Selecting police super-recognisers

Dear Dr. White:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Rocco Palumbo

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Appendix. Additional data and analysis.

(DOCX)

Click here for additional data file.^{(79.4KB, docx)}

Attachment

Submitted filename: SuperRecognisersNSWPF_ResponseToReviewers.pdf

Click here for additional data file.^{(160.2KB, pdf)}

Attachment

Submitted filename: SuperRecognisers_NSWPF_responseToReviewersR2.docx

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Data Availability Statement

The data that support the findings of this study are openly available on the Open Science Framework (https://doi.org/10.17605/OSF.IO/BJDWK).

[pone.0283682.ref001] 1.Fussey P, Davies B, Innes M. ‘Assisted’ facial recognition and the reinvention of suspicion and discretion in digital policing. The British Journal of Criminology. 2021;61(2):325–44. [Google Scholar]

[pone.0283682.ref002] 2.Kemp RI, Towell N, Pike G. When Seeing should not be Believing: Photographs, Credit Cards and Fraud. Applied Cognitive Psychology. 1997;11:211–22. [Google Scholar]

[pone.0283682.ref003] 3.Phillips PJ, Yates AN, Hu Y, Hahn CA, Noyes E, Jackson K, et al. Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms. Proc Natl Acad Sci U S A. 2018;115(24):6171–6. doi: 10.1073/pnas.1721355115 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0283682.ref004] 4.Burton AM, White D, McNeill A. The Glasgow Face Matching Test. Behav Res Methods. 2010;42(1):286–91. doi: 10.3758/BRM.42.1.286 [DOI] [PubMed] [Google Scholar]

[pone.0283682.ref005] 5.White D, Towler A, Kemp RI. Understanding professional expertise in unfamiliar face matching. In: Bindemann M, editor. Forensic Face Matching: Research and Practice: Oxford Scholarship Online; 2021. [Google Scholar]

[pone.0283682.ref006] 6.Duchaine BC, Nakayama K. Developmental prosopagnosia: a window to content-specific face processing. Curr Opin Neurobiol. 2006;16(2):166–73. doi: 10.1016/j.conb.2006.03.003 [DOI] [PubMed] [Google Scholar]

[pone.0283682.ref007] 7.Russell R, Duchaine B, Nakayama K. Super-recognizers: people with extraordinary face recognition ability. Psychon Bull Rev. 2009;16(2):252–7. doi: 10.3758/PBR.16.2.252 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0283682.ref008] 8.White D, Burton AM. Individual differences and the multidimensional nature of face perception. Nature Reviews Psychology. 2022;1(5):287–300. [Google Scholar]

[pone.0283682.ref009] 9.Bate S, Portch E, Mestry N. When two fields collide: Identifying "super-recognisers" for neuropsychological and forensic face recognition research. Q J Exp Psychol (Hove). 2021;74(12):2154–64. doi: 10.1177/17470218211027695 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0283682.ref010] 10.Duchaine B, Nakayama K. The Cambridge Face Memory Test: results for neurologically intact individuals and an investigation of its validity using inverted face stimuli and prosopagnosic participants. Neuropsychologia. 2006;44(4):576–85. doi: 10.1016/j.neuropsychologia.2005.07.001 [DOI] [PubMed] [Google Scholar]

[pone.0283682.ref011] 11.Wilmer JB, Germine L, Chabris CF, Chatterjee G, Williams M, Loken E, et al. Human face recognition ability is specific and highly heritable. Proc Natl Acad Sci U S A. 2010;107(11):5238–41. doi: 10.1073/pnas.0913053107 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0283682.ref012] 12.Zhu Q, Song Y, Hu S, Li X, Tian M, Zhen Z, et al. Heritability of the specific cognitive ability of face perception. Curr Biol. 2010;20(2):137–42. doi: 10.1016/j.cub.2009.11.067 [DOI] [PubMed] [Google Scholar]

[pone.0283682.ref013] 13.Shakeshaft NG, Plomin R. Genetic specificity of face recognition. Proc Natl Acad Sci U S A. 2015;112(41):12887–92. doi: 10.1073/pnas.1421881112 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0283682.ref014] 14.Golarai G, Ghahremani DG, Whitfield-Gabrieli S, Reiss A, Eberhardt JL, Gabrieli JD, et al. Differential development of high-level visual cortex correlates with category-specific recognition memory. Nat Neurosci. 2007;10(4):512–22. doi: 10.1038/nn1865 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0283682.ref015] 15.McGugin RW, Newton AT, Tamber-Rosenau B, Tomarken A, Gauthier I. Thickness of Deep Layers in the Fusiform Face Area Predicts Face Recognition. J Cogn Neurosci. 2020;32(7):1316–29. doi: 10.1162/jocn_a_01551 [DOI] [PubMed] [Google Scholar]

[pone.0283682.ref016] 16.White D, Guilbert D, Varela VPL, Jenkins R, Burton AM. GFMT2: A psychometric measure of face matching ability. Behav Res Methods. 2022;54(1):252–60. doi: 10.3758/s13428-021-01638-x [DOI] [PubMed] [Google Scholar]

[pone.0283682.ref017] 17.McCaffery JM, Robertson DJ, Young AW, Burton AM. Individual differences in face identity processing. Cogn Res Princ Implic. 2018;3:21. doi: 10.1186/s41235-018-0112-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0283682.ref018] 18.Wilmer JB, Germine L, Chabris CF, Chatterjee G, Gerbasi M, Nakayama K. Capturing specific abilities as a window into human individuality: the example of face recognition. Cogn Neuropsychol. 2012;29(5–6):360–92. doi: 10.1080/02643294.2012.753433 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0283682.ref019] 19.Ramon M, Bobak AK, White D. Super-recognizers: From the lab to the world and back again. Br J Psychol. 2019;110(3):461–79. doi: 10.1111/bjop.12368 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0283682.ref020] 20.Davis JP, Lander K, Evans R, Jansari A. Investigating Predictors of Superior Face Recognition Ability in Police Super-recognisers. Applied Cognitive Psychology. 2016;30(6):827–40. [Google Scholar]

[pone.0283682.ref021] 21.Robertson DJ, Noyes E, Dowsett AJ, Jenkins R, Burton AM. Face Recognition by Metropolitan Police Super-Recognisers. PLoS One. 2016;11(2):e0150036. doi: 10.1371/journal.pone.0150036 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0283682.ref022] 22.White D, Dunn JD, Schmid AC, Kemp RI. Error Rates in Users of Automatic Face Recognition Software. PLoS One. 2015;10(10):e0139827. doi: 10.1371/journal.pone.0139827 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0283682.ref023] 23.Towler A, Dunn JD, Martínez SC, Moreton R, Eklöf F, Ruifrok A, et al. Diverse routes to expertise in facial recognition. PsyArXiv. 2021. [Google Scholar]

[pone.0283682.ref024] 24.Rjosk S. Super-Recognizers in (Berlin) law enforcement. Neuropsychologia. 2021;158:107806. doi: 10.1016/j.neuropsychologia.2021.107806 [DOI] [PubMed] [Google Scholar]

[pone.0283682.ref025] 25.Nador JD, Vomland M, Thielgen MM, Ramon M. Face recognition in police officers: Who fits the bill? Forensic Science International: Reports. 2022;5. [Google Scholar]

[pone.0283682.ref026] 26.Balsdon T, Summersby S, Kemp RI, White D. Improving face identification with specialist teams. Cogn Res Princ Implic. 2018;3:25. doi: 10.1186/s41235-018-0114-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0283682.ref027] 27.Bobak AK, Dowsett AJ, Bate S. Solving the Border Control Problem: Evidence of Enhanced Face Matching in Individuals with Extraordinary Face Recognition Skills. PLoS One. 2016;11(2):e0148148. doi: 10.1371/journal.pone.0148148 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0283682.ref028] 28.Noyes E, Phillips PJ, O’Toole A. What is a super-recogniser? Face processing: Systems, disorders and cultural differences: Nova Science Publishers Inc; 2017. p. 173–201. [Google Scholar]

[pone.0283682.ref029] 29.Davis JP, Bretfelean LD, Belanova E, Thompson T. Super-recognisers: Face recognition performance after variable delay intervals. Applied Cognitive Psychology. 2020;34(6):1350–68. [Google Scholar]

[pone.0283682.ref030] 30.Hahn CA, Tang LL, Yates AN, Phillips PJ. Forensic facial examiners versus super‐recognizers: Evaluating behavior beyond accuracy. Applied Cognitive Psychology. 2022;36(6):1209–18. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0283682.ref031] 31.Ramon M. Super-Recognizers—a novel diagnostic framework, 70 cases, and guidelines for future work. Neuropsychologia. 2021;158:107809. doi: 10.1016/j.neuropsychologia.2021.107809 [DOI] [PubMed] [Google Scholar]

[pone.0283682.ref032] 32.White D, Phillips PJ, Hahn CA, Hill M, O’Toole AJ. Perceptual expertise in forensic facial image comparison. Proc Biol Sci. 2015;282(1814). doi: 10.1098/rspb.2015.1292 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0283682.ref033] 33.Dunn JD, Summersby S, Towler A, Davis JP, White D. UNSW Face Test: A screening tool for super-recognizers. PLoS One. 2020;15(11):e0241747. doi: 10.1371/journal.pone.0241747 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0283682.ref034] 34.Towler A, White D, Kemp RI. Evaluating the feature comparison strategy for forensic face identification. J Exp Psychol Appl. 2017;23(1):47–58. doi: 10.1037/xap0000108 [DOI] [PubMed] [Google Scholar]

[pone.0283682.ref035] 35.Towler A, Kemp RI, White D. Can Face Identification Ability Be Trained? Evidence for Two Routes to Expertise. Forensic face matching: Research and practice. 2021:89. [Google Scholar]

[pone.0283682.ref036] 36.Noyes E, Hill MQ, O’Toole AJ. Face recognition ability does not predict person identification performance: using individual data in the interpretation of group results. Cogn Res Princ Implic. 2018;3:23. doi: 10.1186/s41235-018-0117-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0283682.ref037] 37.Yovel G O ’Toole AJ. Recognizing People in Motion. Trends Cogn Sci. 2016;20(5):383–95. doi: 10.1016/j.tics.2016.02.005 [DOI] [PubMed] [Google Scholar]

[pone.0283682.ref038] 38.Davis J, Robertson D. Capitalizing on the super-recognition advantage: a powerful, but underutilized, tool for policing and national security agencies. Journal of the Homeland Defense & Security Information Analysis Centre. 2020;7(1):20–5. [Google Scholar]

[pone.0283682.ref039] 39.Jacoby LL. A process dissociation framework: Separating automatic from intentional uses of memory. Journal of Memory and Language. 1991;30(5):513–41. [Google Scholar]

[pone.0283682.ref040] 40.Mandler G. Recognizing—the Judgment of Previous Occurrence. Psychological Review. 1980;87(3):252–71. [Google Scholar]

[pone.0283682.ref041] 41.Gruppuso V, Lindsay DS, Masson ME. I’d know that face anywhere! Psychon Bull Rev. 2007;14(6):1085–9. doi: 10.3758/bf03193095 [DOI] [PubMed] [Google Scholar]

[pone.0283682.ref042] 42.Dunn JD, Varela VPL, Nicholls VI, Papinutto M, White D, Miellet S. Face-Information Sampling in Super-Recognizers. Psychol Sci. 2022;33(9):1615–30. doi: 10.1177/09567976221096320 [DOI] [PubMed] [Google Scholar]

[pone.0283682.ref043] 43.Richler JJ, Tomarken AJ, Sunday MA, Vickery TJ, Ryan KF, Floyd RJ, et al. Individual differences in object recognition. Psychol Rev. 2019;126(2):226–51. doi: 10.1037/rev0000129 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0283682.ref044] 44.Young AW, Noyes E. We need to talk about super-recognizers Invited commentary on: Ramon M., Bobak A. K., & White D. Super-recognizers: From the lab to the world and back again. British Journal of Psychology. Br J Psychol. 2019;110(3):492–4. [DOI] [PubMed] [Google Scholar]

[pone.0283682.ref045] 45.Thielgen MM, Schade S, Bose C. Face processing in police service: the relationship between laboratory-based assessment of face processing abilities and performance in a real-world identity matching task. Cogn Res Princ Implic. 2021;6(1):54. doi: 10.1186/s41235-021-00317-x [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0283682.ref046] 46.Davis JP, Forrest C, Treml F, Jansari A. Identification from CCTV: Assessing police super-recogniser ability to spot faces in a crowd and susceptibility to change blindness. Applied Cognitive Psychology. 2018;32(3):337–53. [Google Scholar]

[pone.0283682.ref047] 47.White D, Kemp RI, Jenkins R, Matheson M, Burton AM. Passport officers’ errors in face matching. PLoS One. 2014;9(8):e103510. doi: 10.1371/journal.pone.0103510 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0283682.ref048] 48.Searston RA, Tangen JM. The Emergence of Perceptual Expertise with Fingerprints Over Time. Journal of Applied Research in Memory and Cognition. 2017;6(4):442–51. [Google Scholar]

[pone.0283682.ref049] 49.Growns B, Dunn JD, Mattijssen E, Quigley-McBride A, Towler A. Match me if you can: Evidence for a domain-general visual comparison ability. Psychon Bull Rev. 2022;29(3):866–81. doi: 10.3758/s13423-021-02044-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0283682.ref050] 50.Ramon M, Miellet S, Dzieciol AM, Konrad BN, Dresler M, Caldara R. Super-Memorizers Are Not Super-Recognizers. PLoS One. 2016;11(3):e0150972. doi: 10.1371/journal.pone.0150972 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0283682.ref051] 51.Elbich DB, Scherf S. Beyond the FFA: Brain-behavior correspondences in face recognition abilities. Neuroimage. 2017;147:409–22. doi: 10.1016/j.neuroimage.2016.12.042 [DOI] [PubMed] [Google Scholar]

[pone.0283682.ref052] 52.Ambrus GG, Eick CM, Kaiser D, Kovacs G. Getting to Know You: Emerging Neural Representations during Face Familiarization. J Neurosci. 2021;41(26):5687–98. doi: 10.1523/JNEUROSCI.2466-20.2021 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0283682.ref053] 53.Towler A, Kemp RI, Burton AM, Dunn JD, Wayne T, Moreton R, et al. Do professional facial image comparison training courses work? PLoS One. 2019;14(2):e0211037. doi: 10.1371/journal.pone.0211037 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Selecting police super-recognisers

James D Dunn

Alice Towler

Richard I Kemp

David White

Roles

Abstract

1 Introduction

2 Method and results

Table 1. Comparison of NSW Police Force staff to normative data on 3 screening tests.

2.1. Comparing police super-recognisers to facial examiners on challenging face identification tests in the lab verifies their superior ability

Fig 1. Super-recognisers’ face matching accuracy benchmarked against student controls and forensic examiners on two challenging tests of face matching.

2.1.1 Facial recognition candidate list test

2.1.2 Selfie-to-passport test

2.1.3 Qualitative differences between super-recognisers and facial examiners in face matching

Fig 2. Super-recogniser performance compared to student controls and facial forensic examiners on the Expertise in Facial Image Comparison Test (EFCT; White, Phillips et al. 2015).

2.2. Establishing the boundaries of super-recognisers’ superior abilities

2.2.1 Matching person identity when the face is not visible

Fig 3. Super-recognisers’ superiority is largest when faces are visible.

2.2.2 Memory for faces and the context in which they are encountered

Fig 4. Super-recognisers show superior face memory in the photoboard recognition memory test.

Fig 5. Accuracy of super-recognisers and students on the face-in place recognition memory test.

2.2.3 UNSW House Test: Super-recognisers’ memory and matching ability for non-face images

Fig 6. Super-recogniser and student accuracy on the UNSW House Test.

2.3. Summary performance profiles show benefits of stricter criteria when selecting groups of super-recognisers

Fig 7. Overall performance of super-recognisers across 7 follow-up face identification tests expressed as z-scores relative to control participant norms.

3 Discussion

4 Materials and methods

4.1 Ethics statement

4.2 Participants

4.3 Screening tests

Cambridge Face Memory Test (CFMT+)

Glasgow Face Matching Test (GFMT)

UNSW Face Test

4.4 Comparing police super-recognisers to facial examiners on challenging face identification tests in the lab verifies their superior ability

4.4.1 Facial recognition candidate list test

4.4.2 Selfie-to-passport test

4.4.3 Expertise in facial comparison test (EFCT)

4.5 Establishing the boundaries of super-recognisers’ superior abilities

4.5.1 Face and body matching test

4.5.2 Photoboard recognition memory test

4.5.3 Face-in-place recognition memory test

4.5.4 UNSW House Test

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Rocco Palumbo

Roles

Author response to Decision Letter 0

Decision Letter 1

Rocco Palumbo

Roles

Author response to Decision Letter 1

Decision Letter 2

Rocco Palumbo

Roles

Acceptance letter

Rocco Palumbo

Roles

Associated Data

Supplementary Materials

Data Availability Statement

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