Matching unfamiliar faces is a difficult task. Here we ask whether it is possible to improve performance by providing multiple images to support matching. In two experiments we observe that accuracy improves as viewers are provided with additional images on which to base their match. This technique leads to fast learning of an individual, but the effect is identity-specific: Despite large improvements in viewers' ability to match a particular person's face, these improvements do not generalize to other faces. Experiment 2 demonstrated that trial-by-trial feedback provided no additional benefits over the provision of multiple images. We discuss these results in terms of familiar and unfamiliar face processing and draw out some implications for training regimes.

Word count (excluding abstract, references, tables, and figure legends): 3743 2 AbstractIt is well-established that matching images of unfamiliar faces is rather error prone. However, there is an important mismatch between face matching in laboratory and realistic settings. All of the currently available face-matching databases were designed to establish the baseline level of unfamiliar face perception. Therefore, target and test images for each face identity have been taken on the same day, minimising within-face variations. In realistic settings, on the other hand, faces do vary, even day to day. This study examined the proficiency of matching images of unfamiliar faces, which were taken on the same day or months apart. In two experiments, same-day images were matched substantially more accurately and faster than different-date photographs using the standard 1-in-10 and 1-in-1 face matching tasks. This suggests that experimental studies on face matching underestimate its difficulty in real-world situations. Photographs of unfamiliar faces seem to be unreliable proofs of identity, especially if the ID documents do not use very recent images of the holders.

In all contemporary societies, photo-identity documents are used routinely for person identification, but this process is surprisingly fallible. Here we show that this problem is not limited to the identification of specific photographs of a person, but transcends three identity cards of the same person with different images. These identity cards varied substantially from each other in how well they could be recognised but identification rates were generally poor. We also present a potential solution to this problem by demonstrating that person identification can be improved when several photographs of the same person are made available.

Face recognition is widely held to rely on 'configural processing', an analysis of spatial relations between facial features. We present three experiments in which viewers were shown distorted faces, and asked to resize these to their correct shape. Based on configural theories appealing to metric distances between features, we reason that this should be an easier task for familiar than unfamiliar faces (whose subtle arrangements of features are unknown). In fact, participants were inaccurate at this task, making between 8% and 13% errors across experiments. Importantly, we observed no advantage for familiar faces: in one experiment participants were more accurate with unfamiliars, and in two experiments there was no difference. These findings were not due to general task difficulty - participants were able to resize blocks of colour to target shapes (squares) more accurately. We also found an advantage of familiarity for resizing other stimuli (brand logos). If configural processing does underlie face recognition, these results place constraints on the definition of 'configural'. Alternatively, familiar face recognition might rely on more complex criteria - based on tolerance to within-person variation rather than highly specific measurement.

The world-wide pivot to remote learning due to the exogenous shocks of COVID-19 across educational institutions has presented unique challenges and opportunities. This study documents the lived experiences of instructors and students and recommends emerging pathways for teaching and learning strategies post-pandemic. Seventy-one instructors and 122 students completed online surveys containing closed and open-ended questions. Quantitative and qualitative analyses were conducted, including frequencies, chi-square tests, Welch Two-Samples t-tests, and thematic analyses. The results demonstrated that with effective online tools, remote learning could replicate key components of content delivery, activities, assessments, and virtual proctored exams.