40 citations · 137 across the 35 of their papers we have counts for
35 papers
Gaze Prediction as Time-Series Forecasting for Virtual Reality Applications: Quantifying Performance Variability and Extreme-Case Errors
Kateryna Melnyk, Lee Friedman, Oleg Komogortsev
Gaze prediction is essential for addressing motion-to-photon latency and ensuring seamless foveated rendering in Virtual Reality. The reliability of gaze forecasting is highly sens…
Evidence for five types of fixation during a random saccade eye tracking task: Implications for the study of oculomotor fatigue
Lee Friedman, Oleg V. Komogortsev
Our interest was to evaluate changes in fixation duration as a function of time-on-task (TOT) during a random saccade task. We employed a large, publicly available dataset. The fre…
Evaluating Eye Movement Biometrics in Virtual Reality: A Comparative Analysis of VR Headset and High-End Eye-Tracker Collected Dataset
Mehedi Hasan Raju, Dillon J Lohr, Oleg V Komogortsev
Previous studies have shown that eye movement data recorded at 1000 Hz can be used to authenticate individuals. This study explores the effectiveness of eye movement-based biometri…
Establishing a Baseline for Gaze-driven Authentication Performance in VR: A Breadth-First Investigation on a Very Large Dataset
Dillon Lohr, Michael J. Proulx, Oleg Komogortsev
This paper performs the crucial work of establishing a baseline for gaze-driven authentication performance to begin answering fundamental research questions using a very large data…
Evaluation of Eye Tracking Signal Quality for Virtual Reality Applications: A Case Study in the Meta Quest Pro
Samantha Aziz, Dillon J Lohr, Lee Friedman +1
We present an extensive, in-depth analysis of the eye tracking capabilities of the Meta Quest Pro virtual reality headset using a dataset of eye movement recordings collected from…
Assessing the Privacy Risk of Cross-Platform Identity Linkage using Eye Movement Biometrics
Samantha Aziz, Oleg Komogortsev
The recent emergence of ubiquitous, multi-platform eye tracking has raised user privacy concerns over re-identification across platforms, where a person is re-identified across mul…
Temporal Persistence and Intercorrelation of Embeddings Learned by an End-to-End Deep Learning Eye Movement-driven Biometrics Pipeline
Mehedi Hasan Raju, Lee Friedman, Dillon J Lohr +1
What qualities make a feature useful for biometric performance? In prior research, pre-dating the advent of deep learning (DL) approaches to biometric analysis, a strong relationsh…
A Mechanistic Transform Model for Synthesizing Eye Movement Data with Improved Realism
Henry Griffith, Samantha Aziz, Dillon J Lohr +1
This manuscript demonstrates an improved model-based approach for synthetic degradation of previously captured eye movement signals. Signals recorded on a high-quality eye tracking…
Signal vs Noise in Eye-tracking Data: Biometric Implications and Identity Information Across Frequencies
Mehedi H. Raju, Lee Friedman, Dillon Lohr +1
Prior research states that frequencies below 75 Hz in eye-tracking data represent the primary eye movement termed ``signal'' while those above 75 Hz are deemed ``noise''. This stud…
Filtering Eye-Tracking Data From an EyeLink 1000: Comparing Heuristic, Savitzky-Golay, IIR and FIR Digital Filters
Mehedi H. Raju, Lee Friedman, Troy M. Bouman +1
In a previous report (Raju et al.,2023) we concluded that, if the goal was to preserve events such as saccades, microsaccades, and smooth pursuit in eye-tracking recordings, data w…
Checking the Statistical Assumptions Underlying the Application of the Standard Deviation and RMS Error to Eye-Movement Time Series: A Comparison between Human and Artificial Eyes
Lee Friedman, Timothy Hanson, Hal S. Stern +1
Spatial precision is often measured using the standard deviation (SD) of the eye position signal or the RMS of the sample-to-sample differences (StoS) signal during fixation. As bo…
Determining Which Sine Wave Frequencies Correspond to Signal and Which Correspond to Noise in Eye-Tracking Time-Series
Mehedi H. Raju, Lee Friedman, Troy M. Bouman +1
The Fourier theorem states that any time-series can be decomposed into a set of sinusoidal frequencies, each with its own phase and amplitude. The literature suggests that some fre…
GazeBaseVR, a large-scale, longitudinal, binocular eye-tracking dataset collected in virtual reality
Dillon Lohr, Samantha Aziz, Lee Friedman +1
We present GazeBaseVR, a large-scale, longitudinal, binocular eye-tracking (ET) dataset collected at 250 Hz with an ET-enabled virtual-reality (VR) headset. GazeBaseVR comprises 5,…
Analysis of Heuristic and Digital Filters as Applied to Video-oculography Signals
Mehedi H. Raju, Lee Friedman, Troy M. Bouman +1
In 1993, Stampe [1993] suggested two "heurisitic" filters that were designed for video-oculography data. Several manufacturers (e.g., SR-Research, Tobii T60 XL and SMI) have employ…
Demonstrating Eye Movement Biometrics in Virtual Reality
Dillon Lohr, Saide Johnson, Samantha Aziz +1
Thanks to the eye-tracking sensors that are embedded in emerging consumer devices like the Vive Pro Eye, we demonstrate that it is feasible to deliver user authentication via eye m…
Eye Know You Too: A DenseNet Architecture for End-to-end Eye Movement Biometrics
Dillon Lohr, Oleg V Komogortsev
Eye movement biometrics (EMB) is a relatively recent behavioral biometric modality that may have the potential to become the primary authentication method in virtual- and augmented…
An Assessment of the Eye Tracking Signal Quality Captured in the HoloLens 2
Samantha D. Aziz, Oleg V. Komogortsev
We present an analysis of the eye tracking signal quality of the HoloLens 2s integrated eye tracker. Signal quality was measured from eye movement data captured during a random sac…
Eye Know You: Metric Learning for End-to-end Biometric Authentication Using Eye Movements from a Longitudinal Dataset
Dillon Lohr, Henry Griffith, Oleg V Komogortsev
The permanence of eye movements as a biometric modality remains largely unexplored in the literature. The present study addresses this limitation by evaluating a novel exponentiall…
GazeBase: A Large-Scale, Multi-Stimulus, Longitudinal Eye Movement Dataset
Henry Griffith, Dillon Lohr, Evgeny Abdulin +1
This manuscript presents GazeBase, a large-scale longitudinal dataset containing 12,334 monocular eye-movement recordings captured from 322 college-aged subjects. Subjects complete…
Hierarchical HMM for Eye Movement Classification
Ye Zhu, Yan Yan, Oleg Komogortsev
In this work, we tackle the problem of ternary eye movement classification, which aims to separate fixations, saccades and smooth pursuits from the raw eye positional data. The eff…
Parallel Oculomotor Plant Mathematical Model for Large Scale Eye Movement Simulation
Alex Karpov, Jacob Liberman, Dillon Lohr +1
The usage of eye tracking sensors is expected to grow in virtual (VR) and augmented reality (AR) platforms. Provided that users of these platforms consent to employing captured eye…
Benefits of temporal information for appearance-based gaze estimation
Cristina Palmero, Oleg V. Komogortsev, Sachin S. Talathi
State-of-the-art appearance-based gaze estimation methods, usually based on deep learning techniques, mainly rely on static features. However, temporal trace of eye gaze contains u…
OpenEDS2020: Open Eyes Dataset
Cristina Palmero, Abhishek Sharma, Karsten Behrendt +3
We present the second edition of OpenEDS dataset, OpenEDS2020, a novel dataset of eye-image sequences captured at a frame rate of 100 Hz under controlled illumination, using a virt…
Why Temporal Persistence of Biometric Features is so Valuable for Classification Performance
Lee Friedman, Hal Stern, Larry R. Price +1
It is generally accepted that relatively more permanent (i.e., more temporally persistent) traits are more valuable for biometric performance than less permanent traits. Although t…