Oleg Komogortsev

Presidential Early Career Award for Scientists and Engineers

2017

PECASE is "the highest honor bestowed by the United States Government on science and engineering professionals in the early stages of their independent research careers".

Kent State University

PhD

Computer Science

2007

Complex eye movement pattern biometrics: the effects of environment and stimulus

Corey D Holland, Oleg V Komogortsev

2013

This paper presents an objective evaluation of the effects of eye tracking specification and stimulus presentation on the biometric viability of complex eye movement patterns. Six spatial accuracy tiers (0.5°, 1.0°, 1.5°, 2.0°, 2.5°, 3.0°), six temporal resolution tiers (1000, 500, 250, 120, 75, 30 Hz), and five stimulus types (simple, complex, cognitive, textual, random) are evaluated to identify acceptable conditions under which to collect eye movement data. The results suggest the use of eye tracking equipment capable of at least 0.5° spatial accuracy and 250 Hz temporal resolution for biometric purposes, whereas stimulus had little effect on the biometric viability of eye movements.

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Automated classification and scoring of smooth pursuit eye movements in the presence of fixations and saccades

Oleg V. Komogortsev, Alex Karpov

2012

Ternary eye movement classification, which separates fixations, saccades, and smooth pursuit from the raw eye positional data, is extremely challenging. This article develops new and modifies existing eye-tracking algorithms for the purpose of conducting meaningful ternary classification. To this end, a set of qualitative and quantitative behavior scores is introduced to facilitate the assessment of classification performance and to provide means for automated threshold selection. Experimental evaluation of the proposed methods is conducted using eye movement records obtained from 11 subjects at 1000 Hz in response to a step-ramp stimulus eliciting fixations, saccades, and smooth pursuits. Results indicate that a simple hybrid method that incorporates velocity and dispersion thresholding allows producing robust classification performance. It is concluded that behavior scores are able to aid automated threshold selection for the algorithms capable of successful classification.

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Investigating Hologram‐Based Route Planning

Sven Fuhrmann, Oleg Komogortsev, Dan Tamir

2009

It is often assumed that three‐dimensional topographic maps provide more effective route planning, navigation, orientation, and way‐finding results than traditional two‐dimensional representations. The research reported here investigates whether three‐dimensional spatial mappings provide better support for route planning than two‐dimensional representations. In a set of experiments performed as part of this research, human subjects were randomly shown either a two‐ or three‐dimensional hologram of San Francisco and were asked to plan a bicycling route between an origin and a destination point. In a second task, participants used these holograms to identify the highest elevation point in the displayed area. The eye‐movements of the participants, throughout the process of looking at the geospatial holograms and executing the tasks, were recorded. The eye‐tracking metrics analysis indicates with a high statistical level of confidence that three‐dimensional holographic maps enable more efficient route planning. In addition, the research group is developing a new algorithm to analyze the differences between participant‐selected routes and a set of “good routes.” The algorithm employs techniques used to represent the boundary of objects and methods for assessing the difference between objects in modern digital image recognition, image registration, and image alignment applications. The overall goal is to create a theoretical framework for investigating and quantifying route planning effectiveness.

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