Do Two Eyes Identify Better than One? Interpretable Binocular Dynamics for Virtual Reality Gaze Biometrics

Virtual reality (VR) headsets with built-in eye trackers can record binocular gaze, but prior biometric studies have largely emphasized monocular gaze or general spatial trajectories, leaving the identity value of interocular coordination largely untested. We test whether that geometry provides identity information beyond monocular gaze dynamics. Using the GazeBaseVR dataset, we derive disparity, vergence-dynamic, and fixation disparity features and evaluate them in cross-session identification, task transfer, sequence modeling, replay detection, and return-visit analyses. Adding binocular features to monocular features improves cross-session Rank-1 identification, while binocular features alone provide complementary but limited discrimination. Performance is strongest when enrollment and probe tasks match and weakens sharply across tasks. Per-recording disparity centering leaves much of the binocular-only result intact, and a matched-input temporal convolutional network (TCN) performs better with binocular input than with duplicated monocular input. The TCN embedding overlaps with the disparity and vergence features, and simple feature-level fusion does not improve Rank-1 performance. Left-right gaze consistency can reject simple monocular replay attempts in this constrained setting, but score query adaptation reduces detection performance. In this setting, binocular dynamics are best treated as auxiliary evidence for VR gaze biometrics rather than as a standalone authenticator.