Face matching is used in a variety of settings which involves the verification of a person’s identity. Despite being widely used, it is a surprisingly error-prone task. There have been major gains in the accuracy of automated facial recognition algorithms and research has found that fusing algorithm scores with the ratings made by humans can provide almost perfect accuracy on a challenging face-matching task.
However, further research is required to examine how to best form these human-machine teams to carry out face matching tasks. Current projects aim to identify benchmark levels of human and algorithm face matching performance on a dataset of image pairs and explore variations in types of errors made by humans and AI. We will be investigating how we can calibrate trust between humans and AI to facilitate optimal human-machine team performance. Understanding the effect on trust can help people make quicker, more effective, and more accurate decisions with the help of AI and help minimize the risk of errors and misidentification in applied settings.