Facial Identification at a Virtual Reality Airport

Abstract

Person identification at airports requires the comparison of a passport photograph with its bearer. In psychology, this process is typically studied with static pairs of face photographs that require identity-match (same person shown) versus mismatch (two different people) decisions, but this approach provides a limited proxy for studying how environment and social interaction factors affect this task. In this study, we explore the feasibility of virtual reality (VR) as a solution to this problem, by examining the identity matching of avatars in a VR airport. We show that facial photographs of real people can be rendered into VR avatars in a manner that preserves image and identity information (Experiments 1 to 3). We then show that identity matching of avatar pairs reflects similar cognitive processes to the matching of face photographs (Experiments 4 and 5). This pattern holds when avatar matching is assessed in a VR airport (Experiments 6 and 7). These findings demonstrate the feasibility of VR as a new method for investigating face matching in complex environments.

Body Language Influences on Facial Identification at Passport Control: An Exploration in Virtual Reality

“…More importantly, individual performance on the face matching tests also correlated with the VR task. This finding is consistent with a previous validation of this VR paradigm, in which similar correlations were reported between the GFMT, KFMT, and the matching of avatar faces ( Tummon et al., 2019 ). In Experiment 6, however, such correlations were observed only for idle trials.…”

“…Match and mismatch accuracy for these activity levels is then correlated with performance on the GFMT and KFMT. Consistent with previous research ( Tummon et al., 2019 ), identification of idle avatars should correlate with matching performance on the GFMT and KFMT in this design. Conversely, if unusual body language reduces observers’ reliance on facial information of the avatars, then identification performance on lively trials should correlate to a lesser extent with accuracy on the GFMT and KFMT.…”

“…The person stimuli consisted of 200 animated 3D avatars of 100 identities (2 avatars per identity). The 3D avatars were created by combining 2D photographs of real faces, which depicted professional German sportspeople of Caucasian ethnicity and an approximate age of between 20 and 35 years, with an avatar body from an existing database (see Tummon et al., 2019 ). Using graphics software (Artweaver 5), the internal features of a face photograph were mapped onto the features of the avatar’s face area, with the edges smoothed and skin colour adjusted to blend the graphics.…”

“…The 94 match trials were broken down further in 88 noncritical trials and 6 critical match trials, which were used as a direct comparison for the mismatches. The avatar pairings for the critical matches were selected to be comparable to the mismatches for accuracy (mean accuracy matches = 68.1%, SD = 21.7; mean accuracy mismatches = 65.7%, SD = 22.0), based on data from a previous study, t (119) = 0.77, p = .44, d = 0.11 ( Tummon et al., 2019 ). Overall, this process resulted in 100 unique stimulus pairings.…”

“…It is possible to investigate this possibility by comparing performance on the VR passport task with established laboratory tests of face matching ability, such as the Glasgow Face Matching Test (GFMT; Burton et al., 2010 ) and the Kent Face Matching Test (KFMT; Fysh & Bindemann, 2018b ). These tests correlate with a range of facial discrimination and identification tasks (e.g., Fysh, 2018 ; Fysh & Bindemann, 2018b ; McCaffery et al., 2018 ), including the identity matching of the avatars that were employed in the current experiments ( Tummon et al., 2019 ). This indicates that these face tests present suitable and stable measures against which person identification in the VR task can be compared.…”