Kernel deep regression network for touch-stroke dynamics authentication

Inho Chang, Cheng Yaw Low, Seokmin Choi, Andrew Beng Jin Teoh

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1109-1113
Number of pages5
JournalIEEE Signal Processing Letters
Issue number7
Publication statusPublished - 2018 Jul

Bibliographical note

Funding Information:
The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Roberto Caldelli. This work was supported by the National Research Foundation of Korea funded by the Korea government (Ministry of Science, ICT and Future Planning) under Grant 2016R1A2B4011656. (Corresponding author: Andrew Beng-Jin Teoh.) I. Chang, S. Choi, and A. B.-J. Teoh are with the School of Electrical and Electronic Engineering, College of Engineering,Yonsei University, Seoul 120749, South Korea (e-mail:,;;

Publisher Copyright:
© 1994-2012 IEEE.

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics


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