人机协作可显著提升人脸识别准确率，尤其当双方能力相近时。

Human review of consequential decisions by face recognition algorithms creates a collaborative human-machine system. We establish the circumstances under which combining human and machine face identification decisions improves accuracy. Using data from expert and non-expert face identifiers, we show that the benefits of human-human and human-machine collaborations increase as the difference in baseline accuracy between collaborators decreases. This rule holds across a wide range of baseline abilities, from novices to professional forensic face examiners. An important consequence of the rule is that people who are substantially less accurate than the machine, can actually improve decision accuracy when they collaborate with the machine. In a group of individual people collaborating with a machine, "intelligent human-machine fusion" was implemented by selecting people with the potential to increase collaborative accuracy. Performance with intelligent human-machine fusion was more accurate than either the machine operating alone or fusing all humans with the machine. Eliminating the machine from consideration yielded less predictable results, with average performance at or below intelligent human-machine collaboration. However, intelligent human-machine fusion was consistently more effective at minimizing the impact of low-performing humans on accuracy. The results demonstrate a meaningful role for both humans and machines in assuring accurate face identification.