Variation in Photos of the Same Face Drives Improvements in Identity Verification.

People are poor at matching the identity of unfamiliar faces, but very good at identifying familiar faces. Theoretical accounts suggest that representations derived from exposure to variation are instrumental in driving this familiarity based improvement. In support of this, recent work shows that providing multiple photographs of an unfamiliar face improves identity verification accuracy. Here, we test whether the extent of variation is critical to this improvement, by manipulating the degree of within-identity variation that participants are exposed to in a sequential matching test. Participants were more accurate and adopted more liberal response criteria, when matching high-variability pairs to probe images, compared with either low-variability pairs or single images. Importantly, benefits of variation are not explained by independent contributions of single images, suggesting that people extrapolate information across images to produce gains in identification accuracy. These results suggest that photo-ID can be improved by incorporating broader ranges of variation in facial appearance.