Fusing continuous gaze offset scores with standard eye movement features via linear and nonlinear methods improves authentication performance on lab-grade and VR eye-tracking datasets.
Salvucci and Joseph H
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
method 1
citation-polarity summary
fields
cs.HC 1years
2026 1verdicts
UNVERDICTED 1roles
method 1polarities
use method 1representative citing papers
citing papers explorer
-
Enhancing Eye Movement Biometrics for User Authentication via Continuous Gaze Offset Score Fusion
Fusing continuous gaze offset scores with standard eye movement features via linear and nonlinear methods improves authentication performance on lab-grade and VR eye-tracking datasets.