Staff Members - Benha University
Alghamdi S.J. and Lamiaa A. Elrefaei, “Dynamic User Verification Using Touch Keystroke Based on Medians Vector Proximity”, 7th International Conference on Computational Intelligence, Communication Systems and Networks (CICSyN 2015). p. 121-126, June 3-5, 2015, Riga, Latvia, DOI: 10.1109/CICSyN.2015.31
7th International Conference on Computational Intelligence, Communication Systems and Networks (CICSyN 2015) • 2015
Publication Information
Authors
Shatha J. Alghamdi, Lamiaa A. Elrefaei
Keywords
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Journal
7th International Conference on Computational Intelligence, Communication Systems and Networks (CICSyN 2015)
Publisher
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Volume
Not Available
Issue
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Pages
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publication.type
International
Paper Link
Open Link
Supplementary Materials
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Abstract
In this paper a user verification system on mobile
phones is proposed. This system is based on behavioral
biometric traits which is a keystroke dynamics derived from a
touchable keyboard. A mobile application is developed for
collecting those touch keystroke dynamics. In contrast to other
systems, no specific text or numbers are used to build our
dataset. The Median Vector Proximity classifier is applied on
the touch keystroke data (touchable keyboard) and the
performance of the system is investigated using different
number of features and we found that the system with 31
features gained an average EER=12.9%. While with an extra
two features (average of finger size and pressure) the average
EER=12.2%. This shows that the more features used results in
more accurate systems. The proposed system is compared
against other systems and shows promising results in dynamic
authentication area.
phones is proposed. This system is based on behavioral
biometric traits which is a keystroke dynamics derived from a
touchable keyboard. A mobile application is developed for
collecting those touch keystroke dynamics. In contrast to other
systems, no specific text or numbers are used to build our
dataset. The Median Vector Proximity classifier is applied on
the touch keystroke data (touchable keyboard) and the
performance of the system is investigated using different
number of features and we found that the system with 31
features gained an average EER=12.9%. While with an extra
two features (average of finger size and pressure) the average
EER=12.2%. This shows that the more features used results in
more accurate systems. The proposed system is compared
against other systems and shows promising results in dynamic
authentication area.