no code implementations • 26 Feb 2024 • Mehedi Hasan Raju, Lee Friedman, Dillon J Lohr, Oleg V Komogortsev
This paper expands on the foundational concept of temporal persistence in biometric systems, specifically focusing on the domain of eye movement biometrics facilitated by machine learning.
no code implementations • 13 Feb 2023 • Lee Friedman, Timothy Hanson, Hal S. Stern, Oleg V. Komogortsev
For TXstate, 3 of 37 fixations met all assumptions.
no code implementations • 15 Apr 2019 • Evgeniy Abdulin, Lee Friedman, Oleg Komogortsev
Images can be processed offline for the detection of ocular features, including the pupil and corneal reflection (First Purkinje Image, P1) position.
no code implementations • 8 Sep 2017 • Evgeny Abdulin, Lee Friedman, Oleg V. Komogortsev
We refer to this noise as RIONEPS (Rapid Irregularly Oscillating Noise of the Eye Position Signal).
no code implementations • 29 Jul 2017 • Lee Friedman, Oleg Komogortsev
Finally, we use our synthetic database strategy to determine how many features are required to achieve particular levels of performance as the number of subjects in the database increases from 100 to 10, 000.
no code implementations • 27 Mar 2017 • Ioannis Rigas, Lee Friedman, Oleg Komogortsev
This work presents a study on the extraction and analysis of a set of 101 categories of eye movement features from three types of eye movement events: fixations, saccades, and post-saccadic oscillations.
no code implementations • 13 Sep 2016 • Lee Friedman, Ioannis Rigas, Mark S. Nixon, Oleg V. Komogortsev
We suggest that the best way to assess temporal persistence is to perform a test-retest study, and assess test-retest reliability.