Al-Zaidawi, Sahar
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Item-typ:Veröffentlichung, Machine learning classification of user attributes via eye movements(2022-04-22); ; ; The advent of modern eye tracking devices has spawned a plethora of new research on eye movements. Applications of these research results include the prediction of diseases, of biometrics, of gender, or of cognitive developments in children. One par- ticularly well studied topic is user identification. Another, less well studied one is gender prediction. In this thesis, a common framework to predict users and gen- der is proposed. Using this framework, we were able to improve the state-of-the-art accuracies for both user identification and gender prediction. Further, unlike previ- ous studies, the proposed approach was tested with different datasets consisting of varying stimuli. We identify several factors that affect the identification accuracy. Our main improvements in identification accuracy are due to three factors, select- ing optimal hyper-parameters of the segmentation algorithm, adding higher-order derivatives, and including blink information. For gender prediction, the thesis es- tablishes several new insights. For instance, that gender prediction is possible for prepubescent children aged 9–10. Previous research had suggested that significant gender differences in eye movements can only be observed in adults. Various factors are identified which affect the accuracy of gender prediction; for example, the length of the gaze trajectory, possible fatigue of the participant (gender prediction works better in the presence of fatigue), and the choice of feature ranking algorithms.Dissertation380 215 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, Robustness of Eye Movement Biometrics Against Varying Stimuli and Varying Trajectory LengthRecent 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.Konferenzbeitrag103 115