Robustness of Eye Movement Biometrics Against Varying Stimuli and Varying Trajectory Length
Date Issued
2020
Abstract
Recent results suggest that biometric identification based on human's eye movement characteristics can be used for authentication. In this paper, we present three new methods and benchmark them against the state-of-the-art. The best of our new methods improves the state-of-the-art performance by 5.2 percentage points. Furthermore, we investigate some of the factors that affect the robustness of the recognition rate of different classifiers on gaze trajectories, such as the type of stimulus and the tracking trajectory length. We find that the state-of-the-art method only works well when using the same stimulus for testing that was used for training. By contrast, our novel method more than doubles the identification accuracy for these transfer cases. Furthermore, we find that with only 90 seconds of eye tracking data, 86.7% accuracy can be achieved.
Subjects
Computing methodologies
;
Artificial Intelligence
;
Computer Vision
;
Computer vision tasks
;
Biometrics
;
Machine Learning
;
Machine learning algorithms
Institution
Type
conference paper
Journal/Edited collection
Page number
7
Secondary publication
Yes
Document version
Postprint
License
Alle Rechte vorbehalten
Language
English
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Schroeder-AlZaidawi-Prinzler-Maneth-Zachmann_Robustness-of-Eye-Movement-Biometrics_2020_Accepted-version_PDF-A.pdf
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1.22 MB
Format
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