Redesigning photo-ID to improve unfamiliar face matching performance.

Viewers find it difficult to match photos of unfamiliar faces for identity. Despite this, the use of photographic ID is widespread. In this study we ask whether it is possible to improve face matching performance by replacing single photographs on ID documents with multiple photos or an average image of the bearer. In 3 experiments we compare photo-to-photo matching with photo-to-average matching (where the average is formed from multiple photos of the same person) and photo-to-array matching (where the array comprises separate photos of the same person). We consistently find an accuracy advantage for average images and photo arrays over single photos, and show that this improvement is driven by performance in match trials. In the final experiment, we find a benefit of 4-image arrays relative to average images for unfamiliar faces, but not for familiar faces. We propose that conventional photo-ID format can be improved, and discuss this finding in the context of face recognition more generally.