Crowd effects in unfamiliar face matching

Psychological research shows that humans can not reliably match unfamiliar faces. This presents a practical problem, because identity verification processes in a variety of occupational settings depend on people to perform these tasks reliably. In this context, it is surprising that very few studies have attempted to improve human performance. Here, we investigate whether distributing face matching tasks across groups of individuals might help to solve this problem. Across four studies, we measure the accuracy of the 'crowd' on a standard test of face matching ability and find that aggregating individual responses produces substantial gains in matching accuracy. We discuss the practical implications of this result and also suggest ways in which this approach might be used to improve our understanding of face perception more generally.