Journal
IEEE SIGNAL PROCESSING LETTERS
Volume 26, Issue 7, Pages 1001-1005Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2019.2916420
Keywords
Touch-stroke dynamics; authentication; biometrics; temporal sequences; random regression forest
Categories
Funding
- National Research Foundation of Korea - Korea Government (Ministry of Science, ICT, and Future Planning) [2016R1A2B4011656]
- International Scholar Exchange Fellowship (ISEF) program - Korea Foundation for Advanced Studies (KFAS)
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Touch-stroke dynamics is a relatively recent behavioral biometrics. It authenticates an individual by observing his behavior when swiping a stroke on a smartphone or tablet. Several studies have attempted to determine the optimum authentication accuracy of classifiers, but none of them has used time series or temporal machine learning techniques. We postulate that when a user performs a series of touch strokes in a continuous manner, it can be perceived as a temporal behavior characteristic of the person. In this letter, we propose the use of a temporal regression forest to unearth this hidden but vital temporal information. By incorporating this temporal information in the authentication process, the proposed model is able to achieve average equal error rates of similar to 4.0% and similar to 2.5% on the Serwadda dataset and Frank dataset, respectively.
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