An Extensive Study of User Identification via Eye Movements across Multiple Datasets
This work addresses biometric authentication for security applications, but it is incremental as it builds on an existing method with optimizations.
The paper tackled the problem of user identification via eye movements by analyzing factors affecting accuracy, such as stimulus type and feature additions, resulting in improvements of up to 9% on one dataset.
Several studies have reported that biometric identification based on eye movement characteristics can be used for authentication. This paper provides an extensive study of user identification via eye movements across multiple datasets based on an improved version of method originally proposed by George and Routray. We analyzed our method with respect to several factors that affect the identification accuracy, such as the type of stimulus, the IVT parameters (used for segmenting the trajectories into fixation and saccades), adding new features such as higher-order derivatives of eye movements, the inclusion of blink information, template aging, age and gender.We find that three methods namely selecting optimal IVT parameters, adding higher-order derivatives features and including an additional blink classifier have a positive impact on the identification accuracy. The improvements range from a few percentage points, up to an impressive 9 % increase on one of the datasets.