Abstract

The gesture-based human-computer interface requires new user authentication technique because it does not have traditional input devices like keyboard and mouse. In this paper, we propose a new finger-gesture-based authentication method, where the in-air-handwriting of each user is captured by wearable inertial sensors. Our approach is featured with the utilization of both the content and the writing convention, which are proven to be essential for the user identification problem by the experiments. A support vector machine (SVM) classifier is built based on the features extracted from the hand motion signals. To quantitatively benchmark the proposed framework, we build a prototype system with a custom data glove device. The experiment result shows our system achieve a 0.1% equal error rate (EER) on a dataset containing 200 accounts that are created by 116 users. Compared to the existing gesture-based biometric authentication systems, the proposed method delivers a significant performance improvement.

Original languageEnglish (US)
Title of host publicationIEEE International Joint Conference on Biometrics, IJCB 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages531-537
Number of pages7
Volume2018-January
ISBN (Electronic)9781538611241
DOIs
StatePublished - Jan 29 2018
Event2017 IEEE International Joint Conference on Biometrics, IJCB 2017 - Denver, United States
Duration: Oct 1 2017Oct 4 2017

Other

Other2017 IEEE International Joint Conference on Biometrics, IJCB 2017
CountryUnited States
CityDenver
Period10/1/1710/4/17

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Instrumentation
  • Signal Processing
  • Biomedical Engineering

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  • Cite this

    Lu, D., Xu, K., & Huang, D. (2018). A data driven in-air-handwriting biometric authentication system. In IEEE International Joint Conference on Biometrics, IJCB 2017 (Vol. 2018-January, pp. 531-537). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BTAS.2017.8272739