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Oleg  Komogortsev - Michigan State University. East Lansing, MI, US

Oleg Komogortsev Oleg  Komogortsev

Computer Science and Engineering | Michigan State University


Expert in STEM, with research focus in muscle structure of the eye as a basis for identification.






Research | Eye on the Future



Presidential Early Career Award for Scientists and Engineers (PECASE) 2017. The award was given for my work in cybersecurity with the emphasis on eye movement-driven biometrics and health assessment. 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". Press release from White House and press release from the National Science Foundation. NSF statesd that the award was given: "For his groundbreaking research on the muscle structure of the eye as a basis for identification, which is transforming approaches to security and medicine. And for his unparalleled outreach on STEM research and education to large populations of underrepresented minorities."

Industry Expertise (4)

Health Care - Services Advanced Medical Equipment Health and Wellness Computer Hardware

Areas of Expertise (5)

Bioengineering Human Computer Interaction Biometrics Cybersecurity eyetracking

Accomplishments (1)

Presidential Early Career Award for Scientists and Engineers (professional)


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".

Education (1)

Kent State University: PhD, Computer Science 2007

Affiliations (1)

  • Texas State University

News (1)

Computer scientist sees new possibilities for ocular biometrics

National Science Foundation  online


"With support from the National Science Foundation (NSF), computer scientist Oleg Komogortsev and a team at Texas State University are taking the technology a step further, making it even more secure, reliable and nearly impossible to fool.

They are developing a three-layered, multi-biometric approach that tracks the movement of the eye globe and its muscles, and monitors how and where a person's brain focuses visual attention, in addition to scanning patterns in the iris. The iris is the colored part of the eye."

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Journal Articles (3)

Complex eye movement pattern biometrics: the effects of environment and stimulus IEEE Transactions on Information Forensics and Security

Corey D Holland, Oleg V Komogortsev


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 Behavior Research Methods

Oleg V. Komogortsev, Alex Karpov


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 Transactions in GIS

Sven Fuhrmann, Oleg Komogortsev, Dan Tamir


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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