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This paper introduces a novel eye movement dataset collected in virtual reality (VR) that contains both 2D and 3D eye movement data from over 400 subjects. We establish that this dataset is suitable for biometric studies by evaluating it with both statistical and machine learning–based approaches. For comparison, we also include results from an existing, similarly constructed dataset. 

Metric learning is a valuable technique for enabling the ongoing enrollment of new users within biometric systems. While this approach has been heavily employed for other biometric modalities such as facial recognition, applications to eye movements have only recently been explored. This manuscript further investigates the application of metric learning to eye movement biometrics. A set of three multilayer perceptron networks are trained for embedding feature vectors describing three classes of eye movements: fixations, saccades, and post-saccadic oscillations. The network is validated on a dataset containing eye movement traces of 269 subjects recorded during a reading task. The proposed algorithm is benchmarked against a previously introduced statistical biometric approach. While mean equal error rate (EER) was increased versus the benchmark method, the proposed technique demonstrated lower dispersion in EER across the four test folds considered herein.