Global Feature Analysis and Comparative Evaluation of Freestyle In-Air-Handwriting Passcode for User Authentication

Duo Lu, Yuli Deng, Dijiang Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Freestyle in-air-handwriting passcode-based user authentication methods address the needs for Virtual Reality (VR)/Augmented Reality (AR) headsets, wearable devices, and game consoles where a physical keyboard cannot be provided for typing a password, but a gesture input interface is readily available. Such an authentication system can capture the hand movement of writing a passcode string in the air and verify the user identity using both the writing content (like a password) and the writing style (like a behavior biometric trait). However, distinguishing handwriting signals from different users is challenging in signal processing, feature extraction, and matching. In this paper, we provide a detailed analysis of the global features of in-air-handwriting signals and a comparative evaluation of such a user authentication framework. Also, we build a prototype system with two different types of hand motion capture devices, collect two datasets, and conduct an extensive evaluation.

Original languageEnglish (US)
Title of host publicationProceedings - 37th Annual Computer Security Applications Conference, ACSAC 2021
PublisherAssociation for Computing Machinery
Pages468-481
Number of pages14
ISBN (Electronic)9781450385794
DOIs
StatePublished - Dec 6 2021
Event37th Annual Computer Security Applications Conference, ACSAC 2021 - Virtual, Online, United States
Duration: Dec 6 2021Dec 10 2021

Publication series

NameACM International Conference Proceeding Series

Conference

Conference37th Annual Computer Security Applications Conference, ACSAC 2021
Country/TerritoryUnited States
CityVirtual, Online
Period12/6/2112/10/21

Keywords

  • Gesture input interface
  • In-air-handwriting
  • User authentication

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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