Dissociating gait from static appearance: A virtual reality study of the role of dynamic identity signatures in person recognition

Noa Simhi*, Galit Yovel

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Studies on person recognition have primarily examined recognition of static faces, presented on a computer screen at a close distance. Nevertheless, in naturalistic situations we typically see the whole dynamic person, often approaching from a distance. In such cases, facial information may be less clear, and the motion pattern of an individual, their dynamic identity signature (DIS), may be used for person recognition. Studies that examined the role of motion in person recognition, presented videos of people in motion. However, such stimuli do not allow for the dissociation of gait from face and body form, as different identities differ both in their gait and static appearance. To examine the contribution of gait in person recognition, independently from static appearance, we used a virtual environment, and presented across participants, the same face and body form with different gaits. The virtual environment also enabled us to assess the distance at which a person is recognized as a continuous variable. Using this setting, we assessed the accuracy and distance at which identities are recognized based on their gait, as a function of gait distinctiveness. We find that the accuracy and distance at which people were recognized increased with gait distinctiveness. Importantly, these effects were found when recognizing identities in motion but not from static displays, indicating that DIS rather than attention, enabled more accurate person recognition. Overall these findings highlight that gait contributes to person recognition beyond the face and body and stress an important role for gait in real-life person recognition.

Original languageEnglish
Article number104445
StatePublished - Dec 2020


  • Distinctiveness
  • Dynamic identity signatures
  • Gait
  • Person recognition
  • Virtual reality


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