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Author Komogortsev, O.V. ♦ Holland, C.D.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2013
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods ♦ Natural sciences & mathematics ♦ Life sciences; biology ♦ Biochemistry
Subject Keyword Support vector machines ♦ Measurement ♦ Accuracy ♦ Error analysis ♦ Entropy ♦ Compounds
Abstract This paper presents an objective evaluation of previously unexplored biometric techniques utilizing patterns identifiable in complex oculomotor behavior to distinguish individuals. Considered features include: saccadic dysmetria, compound saccades, dynamic overshoot, and express saccades. Score-level information fusion is applied and evaluated by: likelihood ratio, support vector machine, and random forest. The results suggest that it is possible to obtain equal error rates of 25% and rank-1 identification rates of 47% using score-level fusion by likelihood ratio.
Description Author affiliation: Texas State Univ. - San Marcos, San Marcos, TX, USA (Komogortsev, O.V.; Holland, C.D.)
ISBN 9781479905270
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2013-09-29
Publisher Place USA
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 2.68 MB
Page Count 8
Starting Page 1
Ending Page 8

Source: IEEE Xplore Digital Library