Improving face identification with specialist teams

Abstract

People vary in their ability to identify faces, and this variability is relatively stable across repeated testing. This suggests that recruiting high performers can improve identity verification accuracy in applied settings. Here, we report the first systematic study to evaluate real-world benefits of selecting high performers based on performance in standardized face identification tests. We simulated a recruitment process for a specialist team tasked with detecting fraudulent passport applications. University students (n = 114) completed a battery of screening tests followed by a real-world face identification task that is performed routinely when issuing identity documents. Consistent with previous work, individual differences in the real-world task were relatively stable across repeated tests taken 1 week apart (r = 0.6), and accuracy scores on screening tests and the real-world task were moderately correlated. Nevertheless, performance gains achieved by selecting groups based on screening tests were surprisingly small, leading to a 7% improvement in accuracy. Statistically aggregating decisions across individuals—using a ‘wisdom of crowds’ approach—led to more substantial gains than selection alone. Finally, controlling for individual accuracy of team members, the performance of a team in one test predicted their performance in a subsequent test, suggesting that a ‘good team’ is not only defined by the individual accuracy of team members. Overall, these results underline the need to use a combination of approaches to improve face identification performance in professional settings.Electronic supplementary materialThe online version of this article (10.1186/s41235-018-0114-7) contains supplementary material, which is available to authorized users.

Searching for faces in crowd chokepoint videos

“…Although unrelated to our searching task, we considered the relationship between face matching and memory abilities for completeness. Previous research has suggested a moderate to large association between the GFMT and CFMT ( r s = .39—Balsdon, Summersby, Kemp, & White, ; r = .47—Robertson, Jenkins, & Burton, ; r = .45 to .50—McCaffery et al, ). Here, analysing data from our whole sample rather than the unfamiliar judges only (given that no judges were familiar with the models in these two tests), we found a moderate correlation between the two measures of overall percentage correct, r (94) = .34, p < .001.…”

The consistency of superior face recognition skills in police officers

“…Thus, it may be that the ability to complete more challenging face recognition tasks reflects properties of the images themselves, rather than different individuals being suited to different tasks. In any case, wider screening of personnel using tasks that directly replicate real‐world needs should be initiated (see Balsdon, Summersby, Kemp, & White, ), and future work might examine the limits of super recognition with regard to image variability.…”

Super‐recognizers: From the lab to the world and back again

“…While the correlation between laboratory‐based and real‐world measures is often difficult to estimate, it is important to quantify where possible. Balsdon, Summersby, Kemp, and White () measured the correlation between the short version of the Cambridge Face Memory Test (CFMT short; 72 items; Duchaine & Nakayama, ), the Glasgow Face Matching Test (GFMT), and a task designed to simulate passport issuance officers’ actual task (i.e., reviewing passport image arrays to decide whether any of the images matched the passport applicant). The CFMT short and the GFMT showed correlations with this real‐world task of r = .41 and r = .46, respectively .…”