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Biometric verification of a user based on eye movements

Biometric verification of a user based on eye movements The biometric verification of users of computers or other machines is usually performed with fingerprints, face, iris or palm images. Eye movements have seldom been studied for biometric verification, although in the future their use will perhaps extend from laboratory applications to integrated parts of computer interfaces. Eye movements have long been studied in medical and psychological applications. We noticed that there are differences between saccade eye movements of individuals, even in a group of young people approximately of the same age. We measured saccades from 68 subjects by performing the same stimulation for each to obtain comparable data. We tested two verification conditions: a) an authenticated user vs. all other subjects and b) an impostor vs. an authenticated user and others. Randomised classifications with discriminant analysis, k-d tree and k nearest-neighbour searching, decision trees and the naïve Bayesian rule were run, but logistic discriminant analysis produced the best results. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Biometrics Inderscience Publishers

Biometric verification of a user based on eye movements

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1755-8301
eISSN
1755-831X
DOI
10.1504/IJBM.2014.060967
Publisher site
See Article on Publisher Site

Abstract

The biometric verification of users of computers or other machines is usually performed with fingerprints, face, iris or palm images. Eye movements have seldom been studied for biometric verification, although in the future their use will perhaps extend from laboratory applications to integrated parts of computer interfaces. Eye movements have long been studied in medical and psychological applications. We noticed that there are differences between saccade eye movements of individuals, even in a group of young people approximately of the same age. We measured saccades from 68 subjects by performing the same stimulation for each to obtain comparable data. We tested two verification conditions: a) an authenticated user vs. all other subjects and b) an impostor vs. an authenticated user and others. Randomised classifications with discriminant analysis, k-d tree and k nearest-neighbour searching, decision trees and the naïve Bayesian rule were run, but logistic discriminant analysis produced the best results.

Journal

International Journal of BiometricsInderscience Publishers

Published: Jan 1, 2014

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