On the Relationship between Eye Tracking Resolution and Performance of Oculomotoric Biometric Identification

Abstract

Distributional properties of fixations and saccades are known to constitute biometric characteristics. Additionally, high-frequency micro-movements of the eyes have recently been found to constitute biometric characteristics that allow for faster and more robust biometric identification than just macro-movements. Micro-movements of the eyes occur on scales that are very close to the precision of currently available eye trackers. This study therefore characterizes the relationship between the temporal and spatial resolution of eye tracking recordings on one hand and the performance of a biometric identification method that processes micro-and macro-movements via a deep convolutional network. We find that that the deteriorating effects of decreasing both, the temporal and spatial resolution are not cumulative. We observe that on low-resolution data, the network reaches performance levels above chance and outperforms statistical approaches.