Towards a novel technique for identification based on eye tracking

Cantoni, Virginio; Porta, Marco; Maio, Luigi De; Distasi, Riccardo; Nappi, Michele

doi:10.1109/bioms.2012.6345780

Cited by 5 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Eye-movements analysis was first introduced for person authentication in 2004 by Kasprowski and Ober who applied voice recognition techniques to analyze gaze data using popular classification algorithms such kNN, Support Vector Machines, and naïve Bayes classifiers [3]. As eyetracking technology keeps improving, there is great activity related to using gaze as a behavioral biometric modality [e.g., [4][5][6][7][8][9][10]. Studies differ with respect to the proposed data analytics and perceptual tasks involved.…”

Section: Introductionmentioning

confidence: 99%

Gaze as a biometric

2014

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Gaze as a biometric

2014

View full text Add to dashboard Cite

show abstract

“…With the continuous advancement and accessibility of eye-tracking technology, there has been an increased interest in exploring gaze as the source of biometric information by studying different aspects such gaze direction, velocity, etc. [3][4][5][6][7][8][9][10]. By observing how people pursue and achieve various visual tasks, signal processing scientists can derive new biometrics.…”

Section: Introductionmentioning

confidence: 99%

Temporal stability of visual search-driven biometrics

2015

View full text Add to dashboard Cite

Previously, we have shown the potential of using an individual's visual search pattern as a possible biometric. That study focused on viewing images displaying dot-patterns with different spatial relationships to determine which pattern can be more effective in establishing the identity of an individual. In this follow-up study we investigated the temporal stability of this biometric. We performed an experiment with 16 individuals asked to search for a predetermined feature of a random-dot pattern as we tracked their eye movements. Each participant completed four testing sessions consisting of two dot patterns repeated twice. One dot pattern displayed concentric circles shifted to the left or right side of the screen overlaid with visual noise, and participants were asked which side the circles were centered on. The second dot-pattern displayed a number of circles (between 0 and 4) scattered on the screen overlaid with visual noise, and participants were asked how many circles they could identify. Each session contained 5 untracked tutorial questions and 50 tracked test questions (200 total tracked questions per participant). To create each participant's "fingerprint", we constructed a Hidden Markov Model (HMM) from the gaze data representing the underlying visual search and cognitive process. The accuracy of the derived HMM models was evaluated using cross-validation for various time-dependent train-test conditions. Subject identification accuracy ranged from 17.6% to 41.8% for all conditions, which is significantly higher than random guessing (1/16 = 6.25%). The results suggest that visual search pattern is a promising, temporally stable personalized fingerprint of perceptual organization.

show abstract

Eye Movement Analysis in Biometrics

Galdi

Nappi

2018

Biometrics Under Biomedical Considerations

View full text Add to dashboard Cite

Towards a novel technique for identification based on eye tracking

Cited by 5 publications

References 6 publications

Gaze as a biometric

Gaze as a biometric

Temporal stability of visual search-driven biometrics

Eye Movement Analysis in Biometrics

Contact Info

Product

Resources

About