Abstract
People have the ability to purposely transform the appearance of the facial region with the application of make-up, the growing or shaving of facial hair, the addition or removal of glasses, or the alteration of a hair style or color. All of these different types of transformations have an impact on the ability to recognize a person, though it's unclear how much of an impact, and the degree to which different transformations disrupt recognition. The purpose of this study was to add to existing knowledge about the ability of human subjects to recognize naturalistic faces in disguise. We investigated the effects of different types of attribute changes that altered the appearance of faces from presentation to test, for example the addition or subtraction of eyeglasses. Additionally, the effect of varying levels of familiarity on recognition was examined. Participants were first familiarized by viewing faces three, six, or nine times while performing judgment tasks (e.g., attractive vs. unattractive) with individuals either in disguise (wig and/or glasses), or shown with no disguise. During the testing phase, participants were shown both previously learned and novel individuals, and the faces were shown with and without disguise. Results indicated that any attribute change made from presentation to test lowered identification accuracy, and as the number of attribute changes increased, performance decreased. Eyeglasses hindered recognition, but results indicated little difference between tinted and clear-lens glasses in their effect on performance. The d' scores for addition vs. subtraction of eyeglasses replicated prior work showing that encoding a face with eyeglasses and removing them before the recognition task (subtraction) was more damaging than an addition. Although no significant main effect was found for familiarity, post hoc tests did indicate a significant difference between familiarizing someone three times versus nine times.
This research was funded by NSF Award #0339122 (Enhancing Human Performance), the Perceptual Expertise Network (#15573-S6), a collaborative award from James S. McDonnell Foundation, and by the Temporal Dynamics of Learning Center at UCSD (NSF Science of Learning Center SBE-0542013).