Improving Identity Matching of Newly Encountered Faces: Effects of Multi-image Training

Humans are error-prone at matching identity in photos of unfamiliar faces, especially in ambient images that incorporate natural variability in appearance. Nonetheless, matching faces to photographs is heavily relied upon in applied settings (e.g., when crossing the border). Whereas past training protocols emphasized discriminating highly similar identities, we incorporated within-person variability in appearance during training and in our identity-matching task. On each of five training days, participants learned six images per each of six identities. Accuracy improved on an identity-matching task for new images of trained identities, with no generalization to different identities. Experiment 1b suggests that learning multiple images of each identity was key; we found no significant improvement when training involved learning a single image of 12 identities. Collectively, our results have implications for understanding the process of face learning and improving recognition in applied settings.