Face drawing experience is associated with better face recognition performance and reduced left-side bias in face perception

Abstract

Recent research in computer vision showed that while both analytic (local) and holistic (global) approaches perform well in face recognition, the fusion of the two yields the best performance. Professional face artists/caricaturists mastering both the drawing of individual facial features and whole faces may engage in such fused processing of faces, and thus may excel in face recognition. Zhou et al. (2012) found that art students with face drawing experience showed weaker holistic face processing than non-drawers; nevertheless, the two groups did not differ in sequential face-matching performance. Here we recruited professional face artists, in contrast to art students, in a simultaneous face-matching task with unfamiliar faces that differed in illumination, pose, disguise and expression. In contrast to sequential face-matching, our task reduces the involvement of visual short-term memory, as face artists’ advantage is more likely to be in their perceptual ability than in memory. We recruited 15 face artists and 15 non-drawers. Before the experiment, participants drew a face within 15 minutes. An experienced face artist rated participants’ expertise according to the drawings. We found that face artists outperformed non-drawers in the simultaneous face-matching task (t(28) = 2.56, p <.05). Face drawing expertise was positively correlated with face recognition performance (r = 0.38, p <.05). In addition, while non-drawers judged a face made from two left half-faces more similar to the original face than one from two right half-faces, i.e., the left side bias (right hemisphere dominance) in face perception, face artists had a reduced bias than non-drawers (t(28) = -2.39, p <.05), suggesting reduced global face processing. Face drawing expertise was negatively correlated with the bias (r = -0.47, p <.05). Together, the results suggest that face drawing expertise may shift attention from global to local, which may help face recognition in challenging tasks.