  Reliable and robust user identification and authentication are important and
often necessary requirements for many digital services. It becomes paramount in
social virtual reality (VR) to ensure trust, specifically in digital encounters
with lifelike realistic-looking avatars as faithful replications of real
persons. Recent research has shown that the movements of users in extended
reality (XR) systems carry user-specific information and can thus be used to
verify their identities. This article compares three different potential
encodings of the motion data from head and hands (scene-relative,
body-relative, and body-relative velocities), and the performances of five
different machine learning architectures (random forest, multi-layer
perceptron, fully recurrent neural network, long-short term memory, gated
recurrent unit). We use the publicly available dataset "Talking with Hands" and
publish all code to allow reproducibility and to provide baselines for future
work. After hyperparameter optimization, the combination of a long-short term
memory architecture and body-relative data outperformed competing combinations:
the model correctly identifies any of the 34 subjects with an accuracy of 100%
within 150 seconds. Altogether, our approach provides an effective foundation
for behaviometric-based identification and authentication to guide researchers
and practitioners. Data and code are published under
https://go.uniwue.de/58w1r.
