Investigating the other race effect: Human and computer face matching and similarity judgements

Abstract

ABSTRACT The other race effect (ORE) in part describes how people are poorer at identifying faces of other races compared to own-race faces. While well-established with face memory, more recent studies have begun to demonstrate its presence in face matching tasks, with minimal memory requirements. However, several of these studies failed to compare both races of faces and participants in order to fully test the predictions of the ORE. Here, we utilized images of both Black and White individuals, and Black and White participants, as well as tasks measuring perceptions of face matching and similarity. In addition, human judgements were directly compared with computer algorithms. First, we found only partial support for an ORE in face matching. Second, a deep convolutional neural network (residual network with 29 layers) performed exceptionally well with both races. The DCNN’s representations were strongly associated with human perceptions. Taken together, we found that the ORE was not robust or compelling in our human data, and was absent in the computer algorithms we tested. We discuss our results in the context of ORE literature, and the importance of state-of-the-art algorithms.