Abstract
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.
| Original language | English |
|---|---|
| Article number | 8713391 |
| Pages (from-to) | 1001-1005 |
| Number of pages | 5 |
| Journal | IEEE Signal Processing Letters |
| Volume | 26 |
| Issue number | 7 |
| DOIs | |
| Publication status | Published - 2019 Jul |
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All Science Journal Classification (ASJC) codes
- Signal Processing
- Electrical and Electronic Engineering
- Applied Mathematics
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Touch-stroke dynamics authentication using temporal regression forest. / Ooi, Shih Yin; Teoh, Andrew Beng Jin.
In: IEEE Signal Processing Letters, Vol. 26, No. 7, 8713391, 07.2019, p. 1001-1005.Research output: Contribution to journal › Article
TY - JOUR
T1 - Touch-stroke dynamics authentication using temporal regression forest
AU - Ooi, Shih Yin
AU - Teoh, Andrew Beng Jin
PY - 2019/7
Y1 - 2019/7
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=85066485307&partnerID=8YFLogxK
U2 - 10.1109/LSP.2019.2916420
DO - 10.1109/LSP.2019.2916420
M3 - Article
AN - SCOPUS:85066485307
VL - 26
SP - 1001
EP - 1005
JO - IEEE Signal Processing Letters
JF - IEEE Signal Processing Letters
SN - 1070-9908
IS - 7
M1 - 8713391
ER -