Face learning with multiple images leads to fast acquisition of familiarity for specific individuals.

Matching unfamiliar faces is a difficult task. Here we ask whether it is possible to improve performance by providing multiple images to support matching. In two experiments we observe that accuracy improves as viewers are provided with additional images on which to base their match. This technique leads to fast learning of an individual, but the effect is identity-specific: Despite large improvements in viewers' ability to match a particular person's face, these improvements do not generalize to other faces. Experiment 2 demonstrated that trial-by-trial feedback provided no additional benefits over the provision of multiple images. We discuss these results in terms of familiar and unfamiliar face processing and draw out some implications for training regimes.