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 ∼4.0% and ∼2.5% on the Serwadda dataset and Frank dataset, respectively.
Bibliographical noteFunding Information:
Manuscript received March 17, 2019; revised May 7, 2019; accepted May 7, 2019. Date of publication May 13, 2019; date of current version May 28, 2019. This work was supported in part by the National Research Foundation of Korea funded by the Korea Government (Ministry of Science, ICT, and Future Planning) under Grant 2016R1A2B4011656 and in part by the International Scholar Exchange Fellowship (ISEF) program funded by the Korea Foundation for Advanced Studies (KFAS). The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Sumohana S. Channap-payya. (Corresponding author: Andrew Beng-Jin Teoh.) S. Y. Ooi is with the School of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul 120749, South Korea, and also with the Multimedia University, Melaka 75450, Malaysia (e-mail: email@example.com).
© 2019 IEEE.
All Science Journal Classification (ASJC) codes
- Signal Processing
- Electrical and Electronic Engineering
- Applied Mathematics