LPiTrack: Eye movement pattern recognition algorithm and application to biometric identification

Abstract

A comprehensive nonparametric statistical learning framework, called LPiTrack, is introduced for large-scale eye-movement pattern discovery. The foundation of our data-compression scheme is based on a new Karhunen–Loéve-type representation of the stochastic process in Hilbert space by specially designed orthonormal polynomial expansions. We apply this novel nonlinear transformation-based statistical data-processing algorithm to extract temporal-spatial-static characteristics from eye-movement trajectory data in an automated, robust way for biometric authentication. This is a significant step towards designing a next-generation gaze-based biometric identification system. We elucidate the essential components of our algorithm through data from the second Eye Movements Verification and Identification Competition, organized as a part of the 2014 International Joint Conference on Biometrics.