This paper describes how to identify unique individual readers using their eye-movement patterns. A case study including forty participants was conducted in order to measure eye movement during reading. The proposed biometric method is developed based on an informative and stable eye-movement feature set that gives rise to a high performance multi-class identification model. Multiple individual classifiers are trained and tested on our novel feature set consisting of 28 features that represent basic eye-movement, scan path and pupillary characteristics. We combine three high-accuracy classifiers, namely Multilayer Perception, Logistic, and Logistic Model Tree using the average of probabilities as the combination rule. We reach an overall accuracy of 95.31% and an average Equal Error Rate (EER) of 2.03%. Our approach dramatically outperforms previous methods, making it possible to build eye-movement biometric systems for user identification and personalized interfaces.
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