Identity in the Spotlight: Matching Faces Without Overlapping Features

One-to-one face matching is widely used for identity verification yet becomes difficult with unfamiliar faces or when parts of the face are covered. We investigated whether observers could accurately verify identity from different non-overlapping facial fragments. Each stimulus of our novel face matching task had the appearance of a single face image as revealed through four circular 'spotlights,' one from each quadrant of the face. Observers judged whether the designated target spotlight (from image A) depicted the same identity as the three reference spotlights sampled from a different image (all from image B). Across three experiments, we examined observers' matching performance for familiar and unfamiliar faces under Baseline (Experiment 1), Scrambled (Experiment 2), and Inverted (Experiment 3) spotlight arrangements. Accuracy reliably exceeded chance across all but one condition: familiar inverted faces. A familiar-face advantage emerged under baseline conditions, disappeared when the spatial layout was scrambled, and reversed under inversion —while unfamiliar face accuracy remained robust to these manipulations. These findings demonstrate that observers can exploit latent identity information without direct feature correspondence to achieve above chance accuracy in identification and reveal an unexpected dissociation between familiar and unfamiliar face matching when spatial orientation is disrupted in these highly challenging conditions.