أعضاء هيئة التدريس - جامعة بنها
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
معلومات البحث
المؤلفون
Shatha J. Alghamdi, Lamiaa A. Elrefaei
الكلمات المفتاحية
Not Available
المجلة العلمية
7th International Conference on Computational Intelligence, Communication Systems and Networks (CICSyN 2015)
الناشر
Not Available
المجلد
Not Available
العدد
Not Available
الصفحات
Not Available
publication.type
International
رابط البحث
Open Link
المواد المرفقة
Not Available
الملخص
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.