How Well Do Computer-Generated Faces Tap Face Expertise?

Abstract

The use of computer-generated (CG) stimuli in face processing research is proliferating due to the ease with which faces can be generated, standardised and manipulated. However there has been surprisingly little research into whether CG faces are processed in the same way as photographs of real faces. The present study assessed how well CG faces tap face identity expertise by investigating whether two indicators of face expertise are reduced for CG faces when compared to face photographs. These indicators were accuracy for identification of own-race faces and the other-race effect (ORE)–the well-established finding that own-race faces are recognised more accurately than other-race faces. In Experiment 1 Caucasian and Asian participants completed a recognition memory task for own- and other-race real and CG faces. Overall accuracy for own-race faces was dramatically reduced for CG compared to real faces and the ORE was significantly and substantially attenuated for CG faces. Experiment 2 investigated perceptual discrimination for own- and other-race real and CG faces with Caucasian and Asian participants. Here again, accuracy for own-race faces was significantly reduced for CG compared to real faces. However the ORE was not affected by format. Together these results signal that CG faces of the type tested here do not fully tap face expertise. Technological advancement may, in the future, produce CG faces that are equivalent to real photographs. Until then caution is advised when interpreting results obtained using CG faces.

Differential effects of face-realism and emotion on event-related brain potentials and their implications for the uncanny valley theory

“…Although we did not provide participants with explicit biographical information, as a result of social inferences, the more realistic faces might be perceived as having a unique biographical identity. Behavioural evidence showed that computer generated faces are harder to remember, possibly because they are not encoded as a unique person5253. The noticeable discontinuity between levels 1–4 and 5–6 could also imply a categorical change between realistic and non-realistic characters as shown by classification tasks at a similar stylization level1617.…”

“…Interestingly, in a recent study contrasting real neutral faces with neutral faces of puppets, no differences at the N170 level were observed, while from 400 ms onwards a larger LPP was found for real faces51. This was attributed to the salience and unique identity of a real face and to mentalizing about the depicted individual51, as generally computer generated faces are harder to remember5253. In this vein, manipulating perceived uniqueness or distinctiveness via shape or reflectance manipulations of initially non-distinctive real faces has been found to result in a larger late positivity as well as a better memory performance5455.…”

The unique contributions of perceiver and target characteristics in person perception.

“…A great deal of person perception research uses software to create computer-generated faces for research (e.g., FaceGen; Blanz & Vetter, 1999), as it offers fine-grained experimental control. An obvious concern when using these faces is whether the conclusions generalize to real faces (Crookes et al, 2015). As faces become more standardized (whether via using controlled photographs or even by computer generation), attention might be more focused on certain facial features (i.e., increasing target variance).…”

An Event-related Potential Comparison of Facial Expression Processing between Cartoon and Real Faces

“…However, a stronger LPP was found with regard to real faces starting at 400 ms (Ma, Qian, Hu & Wang, 2017). This effect is because of the significance and uniqueness of the real face as well as the understanding of the portrayed individual (Wheatley, Weinberg, Looser, Moran & Hajcak, 2011) because computer-generated faces are usually more difficult to remember (Balas & Pacella, 2015;Crookes et al, 2015). Bruce and Young (1986) considered facial feature encoding and identify recognition as the second stage of face recognition.…”