Visual Processing of Facial Distinctiveness

Abstract

Three experiments are reported in which the role of facial distinctiveness in line-drawing representations of famous faces has been examined. In the first subjects were asked to select their impressions of best likeness interactively from a continuous range of drawings generated in real-time. On average, 42% of caricatures were considered to be the best likeness of famous individuals. The distinctiveness of the target faces related by independent observers correlated with the degree of exaggeration faces received to produce an optimal likeness. More distinctive faces required less caricaturing. In the second experiment caricature levels chosen as best likenesses were investigated. Caricatures of famous faces were recognised more quickly (by 36% of subjects) and more accurately (by 28%) than the true veridical drawings. Thus caricature enhancement can improve recognition in terms of both speed and accuracy. In the third experiment the role of distinctiveness and its interaction with external and internal facial features was investigated. The external (outer) hairline present in the previous experiment was found to provide a powerful frame of reference for the perceptual assessment of individuals' appearances; estimations of best likeness without external features present were, on average, veridical. It is concluded that accurate line drawings of even highly familiar faces are poor at conveying identity. Distinctiveness enhancement improves their likeness and their recognisability. Such caricatured drawings provide improved access to memories of famous faces, which lends strong support to models of human-face memory and processing based on norm-based coding. When required to decide upon the veracity of line drawings based only upon internal features subjects were able to make accurate judgments of veridicality. As well as validating the interaction approach to best-likeness judgments, this result further indicates the difficulty in making judgments of identification based on piecemeal and sparse configural information.