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Proceedings Paper

Gaze as a biometric
Author(s): Hong-Jun Yoon; Tandy R. Carmichael; Georgia Tourassi
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Paper Abstract

Two people may analyze a visual scene in two completely different ways. Our study sought to determine whether human gaze may be used to establish the identity of an individual. To accomplish this objective we investigated the gaze pattern of twelve individuals viewing still images with different spatial relationships. Specifically, we created 5 visual “dotpattern” tests to be shown on a standard computer monitor. These tests challenged the viewer’s capacity to distinguish proximity, alignment, and perceptual organization. Each test included 50 images of varying difficulty (total of 250 images). Eye-tracking data were collected from each individual while taking the tests. The eye-tracking data were converted into gaze velocities and analyzed with Hidden Markov Models to develop personalized gaze profiles. Using leave-one-out cross-validation, we observed that these personalized profiles could differentiate among the 12 users with classification accuracy ranging between 53% and 76%, depending on the test. This was statistically significantly better than random guessing (i.e., 8.3% or 1 out of 12). Classification accuracy was higher for the tests where the users’ average gaze velocity per case was lower. The study findings support the feasibility of using gaze as a biometric or personalized biomarker. These findings could have implications in Radiology training and the development of personalized e-learning environments.

Paper Details

Date Published: 11 March 2014
PDF: 7 pages
Proc. SPIE 9037, Medical Imaging 2014: Image Perception, Observer Performance, and Technology Assessment, 903707 (11 March 2014); doi: 10.1117/12.2044303
Show Author Affiliations
Hong-Jun Yoon, Oak Ridge National Lab. (United States)
Tandy R. Carmichael, Tennessee Technological Univ. (United States)
Georgia Tourassi, Oak Ridge National Lab. (United States)


Published in SPIE Proceedings Vol. 9037:
Medical Imaging 2014: Image Perception, Observer Performance, and Technology Assessment
Claudia R. Mello-Thoms; Matthew A. Kupinski, Editor(s)

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