Unlocking Smart Phone through Handwaving Biometrics

Lei Yang, Yi Guo, Xuan Ding, Jinsong Han, Yunhao Liu, Cheng Wang, Changwei Hu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

44 Citations (Scopus)

Abstract

Screen locking/unlocking is important for modern smart phones to avoid the unintentional operations and secure the personal stuff. Once the phone is locked, the user should take a specific action or provide some secret information to unlock the phone. The existing unlocking approaches can be categorized into four groups: motion, password, pattern, and fingerprint. Existing approaches do not support smart phones well due to the deficiency of security, high cost, and poor usability. We collect 200 users' handwaving actions with their smart phones and discover an appealing observation: the waving pattern of a person is kind of unique, stable and distinguishable. In this paper, we propose OpenSesame, which employs the users' waving patterns for locking/unlocking. The key feature of our system lies in using four fine-grained and statistic features of handwaving to verify users. Moreover, we utilize support vector machine (SVM) for accurate and fast classification. Our technique is robust compatible across different brands of smart phones, without the need of any specialized hardware. Results from comprehensive experiments show that the mean false positive rate of OpenSesame is around 15 percent, while the false negative rate is lower than 8 percent.
Original languageEnglish
Article number6862001
Pages (from-to)1044-1055
Number of pages12
JournalIEEE Transactions on Mobile Computing
Volume14
Issue number5
DOIs
Publication statusPublished - 1 May 2015
Externally publishedYes

Keywords

  • accelerometer
  • authentication
  • privacy
  • security
  • Smart phone

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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