In recent years, wide deployment of automatic face recognition systems has been accompanied by substantial gains in algorithm performance. However, benchmarking tests designed to evaluate these systems do not account for the errors of human operators, who are often an integral part of face recognition solutions in forensic and security settings. This causes a mismatch between evaluation tests and operational accuracy. We address this by measuring user performance in a face recognition system used to screen passport applications for identity fraud. Experiment 1 measured target detection accuracy in algorithm-generated ‘candidate lists’ selected from a large database of passport images. Accuracy was notably poorer than in previous studies of unfamiliar face matching: participants made over 50% errors for adult target faces, and over 60% when matching images of children. Experiment 2 then compared performance of student participants to trained passport officers–who use the system in their daily work–and found equivalent performance in these groups. Encouragingly, a group of highly trained and experienced “facial examiners” outperformed these groups by 20 percentage points. We conclude that human performance curtails accuracy of face recognition systems–potentially reducing benchmark estimates by 50% in operational settings. Mere practise does not attenuate these limits, but superior performance of trained examiners suggests that recruitment and selection of human operators, in combination with effective training and mentorship, can improve the operational accuracy of face recognition systems.

Facial image comparison practitioners compare images of unfamiliar faces and decide whether or not they show the same person. Given the importance of these decisions for national security and criminal investigations, practitioners attend training courses to improve their face identification ability. However, these courses have not been empirically validated so it is unknown if they improve accuracy. Here, we review the content of eleven professional training courses offered to staff at national security, police, intelligence, passport issuance, immigration and border control agencies around the world. All reviewed courses include basic training in facial anatomy and prescribe facial feature (or ‘morphological’) comparison. Next, we evaluate the effectiveness of four representative courses by comparing face identification accuracy before and after training in novices ( n = 152) and practitioners ( n = 236). We find very strong evidence that short (1-hour and half-day) professional training courses do not improve identification accuracy, despite 93% of trainees believing their performance had improved. We find some evidence of improvement in a 3-day training course designed to introduce trainees to the unique feature-by-feature comparison strategy used by facial examiners in forensic settings. However, observed improvements are small, inconsistent across tests, and training did not produce the qualitative changes associated with examiners’ expertise. Future research should test the benefits of longer examination-focussed training courses and incorporate longitudinal approaches to track improvements caused by mentoring and deliberate practice. In the absence of evidence that training is effective, we advise agencies to explore alternative evidence-based strategies for improving the accuracy of face identification decisions.

We present a new test–the UNSW Face Test (www.unswfacetest.com)–that has been specifically designed to screen for super-recognizers in large online cohorts and is available free for scientific use. Super-recognizers are people that demonstrate sustained performance in the very top percentiles in tests of face identification ability. Because they represent a small proportion of the population, screening large online cohorts is an important step in their initial recruitment, before confirmatory testing via standardized measures and more detailed cognitive testing. We provide normative data on the UNSW Face Test from 3 cohorts tested via the internet (combined n = 23,902) and 2 cohorts tested in our lab (combined n = 182). The UNSW Face Test: (i) captures both identification memory and perceptual matching, as confirmed by correlations with existing tests of these abilities; (ii) captures face-specific perceptual and memorial abilities, as confirmed by non-significant correlations with non-face object processing tasks; (iii) enables researchers to apply stricter selection criteria than other available tests, which boosts the average accuracy of the individuals selected in subsequent testing. Together, these properties make the test uniquely suited to screening for super-recognizers in large online cohorts.

Visual comparison—comparing visual stimuli (e.g., fingerprints) side by side and determining whether they originate from the same or different source (i.e., “match”)—is a complex discrimination task involving many cognitive and perceptual processes. Despite the real-world consequences of this task, which is often conducted by forensic scientists, little is understood about the psychological processes underpinning this ability. There are substantial individual differences in visual comparison accuracy amongst both professionals and novices. The source of this variation is unknown, but may reflect a domain-general and naturally varying perceptual ability. Here, we investigate this by comparing individual differences (N = 248 across two studies) in four visual comparison domains: faces, fingerprints, firearms, and artificial prints. Accuracy on all comparison tasks was significantly correlated and accounted for a substantial portion of variance (e.g., 42% in Exp. 1) in performance across all tasks. Importantly, this relationship cannot be attributed to participants’ intrinsic motivation or skill in other visual-perceptual tasks (visual search and visual statistical learning). This paper provides novel evidence of a reliable, domain-general visual comparison ability.

Facial recognition errors jeopardize national security, criminal justice, public safety and civil rights. Here, we compare the most accurate humans and facial recognition technology in a detailed lab-based evaluation and international proficiency test for forensic scientists involving 27 forensic departments from 14 countries. We find striking cognitive and perceptual diversity between naturally skilled super-recognizers, trained forensic examiners and deep neural networks, despite them achieving equivalent accuracy. Clear differences emerged in super-recognizers’ and forensic examiners’ perceptual processing, errors, and response patterns: super-recognizers were fast, biased to respond ‘same person’ and misidentified people with extreme confidence, whereas forensic examiners were slow, unbiased and strategically avoided misidentification errors. Further, these human experts and algorithms disagreed on the similarity of faces, pointing to differences in their face representations. Our findings reveal there are multiple types of facial recognition expertise, some of which are better suited to particular real-world facial recognition roles than others.

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BACKGROUND: Respiratory syncytial virus (RSV) is the leading cause of upper and lower respiratory tract infections in infants and young children. Most children are exposed to the virus before they are 2 years old and experience such symptoms as cough, fever, and irritability. In a select population of infants, the virus can cause hypoxemia and hospitalization. To avoid hospitalization, good infection control practices should be employed, and for those infants at high risk, prophylaxis with palivizumab is indicated. Palivizumab has been shown to reduce hospitalization rates in high-risk infants by 50%. Because of the high cost of palivizumab, it is prudent to use this medication in the population in which it will be most effective. The American Academy of Pediatrics (AAP) established the criteria for those infants who would benefit the most from palivizumab prophylaxis, and these criteria were the foundation for a prior authorization (PA) program to determine coverage of palivizumab in a health plan of approximately 500,000 members.

Searching for unfamiliar faces in crowds is an important task in modern society. In surveillance and security settings, it is sometimes critical to locate a target individual quickly and accurately. In this study, we examine whether we can improve search efficiency in these visual search tasks by changing the face information that is provided to participants. In Experiment 1, we compare speed and accuracy of visual search when searching for unfamiliar and familiar faces after being exposed to either a single exemplar image or a face average created from multiple images of the target face. In Experiment 2, we compare search efficiency when single exemplars and multiple exemplars are provided. Consistent with studies of unfamiliar face matching tasks, we find that, relative to a single image, having multiple images of the target improves the accuracy of visual search. In Experiment 3, we compared search performance for face averages and multiple exemplars while also varying crowd size. Multiple exemplars conferred an additional advantage over face averages, suggesting that exposure to within-face variability results in the best search performance. We discuss the implications of these findings for face-in-a-crowd search and visual search tasks more generally.Electronic supplementary materialThe online version of this article (10.1186/s41235-018-0128-1) contains supplementary material, which is available to authorized users.