The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Keystroke Biometric Identification and Authentication on Long-Text Input
Abstract
A novel keystroke biometric system for long-text input was developed and evaluated for user identification and authentication applications. The system consists of a Java applet to collect raw keystroke data over the Internet, a feature extractor, and pattern classifiers to make identification or authentication decisions. Experiments on over 100 subjects investigated two input modes–copy and free-text input–and two keyboard types–desktop and laptop keyboards. The system can accurately identify or authenticate individuals if the same type of keyboard is used to produce the enrollment and questioned input samples. Longitudinal experiments quantified performance degradation over intervals of several weeks and over an interval of two years. Additional experiments investigated the system’s hierarchical model, parameter settings, assumptions, and sufficiency of enrollment samples and input-text length. Although evaluated on input texts up to 650 keystrokes, we found that input of 300 keystrokes, roughly four lines of text, is sufficient for the important applications described.
Related Content
|
Ajay Rawat, Shivani Gambhir.
© 2017.
19 pages.
|
|
Abhijit Chandra, Srideep Maity.
© 2017.
15 pages.
|
|
Swanirbhar Majumder, Saurabh Pal.
© 2017.
26 pages.
|
|
Fouad Farouk Jabri.
© 2017.
32 pages.
|
|
Francisco Pacheco Andrade, Teresa Coelho Moreira.
© 2017.
13 pages.
|
|
Swanirbhar Majumder, Smita Majumder.
© 2017.
31 pages.
|
|
Yuanfang Guo, Oscar C. Au, Ketan Tang.
© 2017.
20 pages.
|
|
|