Quantile Layers: Statistical Aggregation in Deep Neural Networks for Eye Movement Biometrics

Abstract

Human eye gaze patterns are highly individually characteristic. Gaze patterns observed during the routine access of a user to a device or document can therefore be used to identify subjects unobtrusively, that is, without the need to perform an explicit verification such as entering a password. Existing approaches to biometric identification from gaze patterns segment raw gaze data into short, local patterns called saccades and fixations. Subjects are then identified by characterizing the distribution of these patterns or deriving hand-crafted features for them. In this paper, we follow a different approach by training deep neural networks directly on the raw gaze data. As the distribution of short, local patterns has been shown to be particularly informative for distinguishing subjects, we introduce a parameterized and end-to-end learnable statistical aggregation layer called the quantile layer that enables the network to explicitly fit the distribution of filter activations in preceding layers. We empirically show that deep neural networks with quantile layers outperform existing probabilistic and feature-based methods for identifying subjects based on eye movements by a large margin.