Masked face identification is improved by diagnostic feature training

Trust in automation and the accuracy of human-algorithm teams performing one-to-one face matching tasks

The human face is commonly used for identity verification. While this task was once exclusively performed by humans, technological advancements have seen automated facial recognition systems (AFRS) integrated into many identification scenarios. Although many state-of-the-art AFRS are exceptionally accurate, they often require human oversight or involvement, such that a human operator actions the final decision. Previously, we have shown that on average, humans assisted by a simulated AFRS (sAFRS) failed to reach the level of accuracy achieved by the same sAFRS alone, due to overturning the system's correct decisions and/or failing to correct sAFRS errors. The aim of the current study was to investigate whether participants' trust in automation was related to their performance on a one-to-one face matching task when assisted by a sAFRS. Participants (n = 160) completed a standard face matching task in two phases: an unassisted baseline phase, and an assisted phase where they were shown the identification decision (95% accurate) made by a sAFRS prior to submitting their own decision. While most participants improved with sAFRS assistance, those with greater relative trust in automation achieved larger gains in performance. However, the average aided performance of participants still failed to reach that of the sAFRS alone, regardless of trust status. Nonetheless, further analysis revealed a small sample of participants who achieved 100% accuracy when aided by the sAFRS. Our results speak to the importance of considering individual differences when selecting employees for roles requiring human-algorithm interaction, including identity verification tasks that incorporate facial recognition technologies.

Face matching and metacognition: investigating individual differences and a training intervention

Background

Although researchers have begun to consider metacognitive insight during face matching, little is known about the underlying mechanism. Here, I investigated whether objective ability, as well as self-assessed ability, were able to predict metacognitive performance, that is, the ability to differentiate correct and incorrect responses in terms of confidence. In addition, I considered whether a training intervention resulted in improvements to both face matching performance and metacognitive insight.

Methods

In this experiment (N = 220), participants completed a face matching task, with either a diagnostic feature training course or a control course presented at the halfway point. In addition, a second face matching task, as well as a self-report questionnaire regarding ability, were completed to provide measures of objective and self-assessed ability respectively.

Results

Higher self-assessed ability with faces, as well as higher objective ability with face matching, predicted better metacognitive performance, i.e., greater confidence in correct, in comparison with incorrect, responses. This pattern of results was evident both when objective ability was measured through performance on the same task used to measure metacognitive insight and when a different task was used. Finally, the training intervention failed to produce improvements in face matching performance and showed no evidence of altering metacognitive ability.

Discussion

The current work begins to address the mechanism underlying individual differences in metacognitive insight during face matching. Although support was provided for a competence-based account, where better face matchers showed greater performance on the task and were more successful in monitoring their performance, further work might focus on decoupling task performance and competence in order to more conclusively explain why some people are more insightful than others.