Search templates that incorporate within-face variation improve visual search for faces

Abstract Searching for unfamiliar faces in crowds is an important task in modern society. In surveillance and security settings, it is sometimes critical to locate a target individual quickly and accurately. In this study, we examine whether we can improve search efficiency in these visual search tasks by changing the face information that is provided to participants. In Experiment 1, we compare speed and accuracy of visual search when searching for unfamiliar and familiar faces after being exposed to either a single exemplar image or a face average created from multiple images of the target face. In Experiment 2, we compare search efficiency when single exemplars and multiple exemplars are provided. Consistent with studies of unfamiliar face matching tasks, we find that, relative to a single image, having multiple images of the target improves the accuracy of visual search. In Experiment 3, we compared search performance for face averages and multiple exemplars while also varying crowd size. Multiple exemplars conferred an additional advantage over face averages, suggesting that exposure to within-face variability results in the best search performance. We discuss the implications of these findings for face-in-a-crowd search and visual search tasks more generally.