Kernel Deep Regression Network for Touch-Stroke Dynamics Authentication


Chang, Inho and Low, Cheng Yaw and Choi, Seokmin and Teoh, Andrew Beng Jin (2018) Kernel Deep Regression Network for Touch-Stroke Dynamics Authentication. IEEE Signal Processing Letters, 25 (7). pp. 1109-1113. ISSN 1070-9908

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—Touch-stroke dynamics is an emerging behavioral biometrics justified feasible for mobile identity management. A touch-stroke dynamics authentication system is composed of a hand-engineered feature extractor and a classifier separately. In this letter, we propose a stacking-based deep learning network that performs feature extraction and classification, collectively dubbed Kernel Deep Regression Network (KDRN). The KDRN is built on multiple kernel ridge regressions (KRR) hierarchically, where each is trained analytically and independently. In principal, KDRN does not mean to learn directly from the raw touch-stroke data like other deep learning models, but it relearns from the pre-extracted features to yield a richer and a relatively more discriminative feature set. Subsequent to that, the authentication is carried out by KRR. Overall, KDRN achieves an equal error rate of 0.013% for intrasession authentication, 0.023% for intersession authentication, and 0.121% for interweek authentication on the Touchlaytics dataset

Item Type: Article
Uncontrolled Keywords: Kernel functions, Authentication, biometrics, stacking-based deep neural network, touch-stroke dynamics
Subjects: Q Science > QA Mathematics > QA299.6-433 Analysis
Divisions: Faculty of Engineering (FOE)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 11 Nov 2020 13:01
Last Modified: 11 Nov 2020 13:01


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