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.