Identifying unfamiliar faces is surprisingly error-prone, even for experienced professionals who perform this task regularly. Previous attempts to train this ability have been largely unsuccessful, leading many to conclude that face identity processing is hard-wired and not amenable to further perceptual learning. Here, we take a novel expert knowledge elicitation approach to training, based on the feature-based comparison strategy used by high-performing professional facial examiners. We show that instructing novices to focus on the facial features that are most diagnostic of identity for these experts--the ears and facial marks (e.g., scars, freckles and blemishes)--improves accuracy on unfamiliar face matching tasks by 6%. This training takes just 6 min to complete and yet accounts for approximately half of experts' superiority on the task. Benefits of training are strongest when diagnostic features are clearly visible and absent when participants are trained to rely on nondiagnostic features. Our data-driven approach contrasts with theory-driven training that is designed to improve holistic face processing mechanisms associated with familiar face recognition. This suggests that protocols which bypass the core face recognition system--and instead reorient attention to features that are undervalued by novices--offer a more promising route to training for unfamiliar face matching.